Treatment and detection of melanoma

ABSTRACT

Provided are methods for determining or predicting the diagnosis, prognosis, treatment and therapeutic efficacy of melanoma in a subject, which include evaluating the expression level of one or more miRNA biomarkers including miRNA-4487, miRNA-4706, miRNA-4731 and fragments or variants thereof as an indication of whether the subject may have, or be predisposed to, melanoma.

TECHNICAL FIELD

THIS INVENTION relates to the prognosis, diagnosis and/or treatment of cancers. More particularly, this invention relates to determining expression levels of one or more micro-RNAs correlated with melanoma in a biological sample from a subject.

BACKGROUND

Melanomas are among the most commonly occurring cancers. Incidence rates in Australia² and the USA³ reveal more than 11,400 (in 2010) and 76,000 (estimated in 2014) new cases respectively, with more than 1,500 (in 2011) and 9,700 (estimated in 2014) respectively, dying annually of advanced disease. Current staging criteria are based on melanoma progression from an early stage lesion, confined to the epidermis (stage 0) followed by a series of early stages of local invasion (I and II) before moving to the regional lymph nodes (stage III) and finally metastasizing to distal sites (stage IV). The overall 5-year survival for melanoma is 91%. However, if distal metastasis occurs (AJCC 7^(th) ed stage IV), cure rates are <15%¹. Hence, melanoma must be detected in earlier stages to maximize the chances of patient survival. Therefore, the ability to rapidly identify predictors of melanoma progression in patients would therefore be a significant clinical tool.

For many years, melanoma progression markers have been studied intensively with varying levels of success. Serum lactate dehydrogenase (LDH) levels have been reported to be elevated in melanoma patients and have been integrated into current staging regimens¹. Despite the high sensitivity and specificity of serum LDH levels in stage II patients (95% and 83% respectively), performance of this marker has been found to be reduced as disease progresses (stage III, 57% and 87%, respectively)⁴⁻⁸. In an earlier study of stage IV patients, sensitivity and specificity was increased to 79% and 92% respectively⁹. S-100B, a calcium binding protein, is another serological marker found to be elevated in stage III and IV melanoma patients^(10, 11). The proportion of patients with elevated S-100B levels has been measured in all stages of disease which vary dependent somewhat upon stage: 0-9% in stage I/II, 5-98% in stage III, and 40-100% in stage IV (reviewed in ref 12). However, serum S 100B is not routinely used in the clinic, highlighting that the current serological methods of progression detection, whilst relatively specific, are inadequate due to somewhat variable sensitivity to detect all stages of disease. To date, there are no biomarkers that are sensitive or specific enough to be beneficial for early diagnosis of melanoma. The use of a blood test (‘circulating’ biomarkers) for early detection of melanoma, prior to distant metastases, could improve treatment and outcomes for melanoma patients.

In order for a circulating biomarker to be effective, it needs to be not only highly sensitive and specific; it also needs to be highly stable and resistant to degradation. In recent years, circulating microRNAs (miRNAs) have been studied intensively for their utility as biomarkers in a wide range of malignancies and disorders¹³⁻¹⁵. miRNAs are small (20-22 nt) non-coding RNAs which function primarily is to regulate gene expression in a sequence specific manner and to act as positive or negative regulators of protein levels in the cell. More recently, tumour cells have been shown experimentally to release miRNAs directly into the circulation¹⁶ contained primarily in micro-vesicles or exosomes (extracellular vesicles), or bound to AGO2 which is part of the miRNA-mediated silencing complex¹³⁻¹⁵. Due to the ‘encapsulation’ of these miRNAs in serum or plasma, they are highly resistant to degradation from RNases (highly concentrated in the blood) thus their potential usefulness as a ‘biomarker’ is relatively high. Not only are they resistant to degradation, they are also inherently stable with many studies highlighting their resistance to extended storage conditions and changes in pH and temperature¹³⁻¹⁵.

To date, the identification of circulating melanoma-specific miRNAs has been limited to a small number of studies^(17, 18). Most recently Friedman et al. (2012) screened 355 miRNAs in sera from 80 melanoma patients using a previously characterised panel of serum-expressed miRNAs¹⁸. The authors found that a five-miRNA signature was able to classify the patients into high and low recurrence risk. Although this study highlighted the potential use of this subset of miRNAs for diagnostic purposes, it only assessed ˜17% of all known miRNAs (there are now >2000 miRNAs in miRBase¹⁹). In addition, the panel of miRNAs selected for validation was not assessed for its specificity to melanoma. Whilst this panel addresses an important question, it is limited to prior diagnosis of melanoma and could not be used to identify the presence of unknown primaries or unidentified metastases. Accordingly, there remains a need for biomarkers, such as miRNA biomarkers, that are useful in the diagnosis, prognosis, therapeutic response monitoring and/or disease monitoring in melanoma patients. Specifically, the use of such biomarkers may address the outstanding clinical needs for more sensitive tools to screen for recurrence following treatment and prognostic tools to guide treatment decisions for patients with advanced disease.

SUMMARY

MicroRNAs represent an important class of biomarkers that provide opportunities for clinical translation. The invention is broadly directed to a method of diagnosis, prognosis, therapeutic response monitoring and/or disease response monitoring of melanoma.

More particularly, the inventors have discovered specific miRNA biomarkers that have proved to be useful in the diagnosis, prognosis and/or disease monitoring of melanoma. Subsequently, methods have been developed to diagnose and/or monitor disease progression in subjects with melanoma as well as to provide an indication of disease prognosis. Furthermore, the inventors have discovered that particular miRNA biomarkers may serve as a prognostic marker with respect to treatment response.

In a first aspect, the invention provides a method of determining whether or not a subject has melanoma, including:

determining an expression level of one or more miRNA biomarkers in a biological sample from a subject, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, or a fragment or variant thereof, and wherein melanoma is detected if said expression level of one or more miRNA biomarkers, is altered or modulated in the biological sample.

In a second aspect, the invention provides a method of determining the prognosis of a subject with melanoma, including:

determining an expression level of one or more miRNA biomarkers in a biological sample obtained from the subject, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, or a fragment or variant thereof, to thereby evaluate the prognosis of melanoma in the subject.

Suitably, if the expression level of said one or more miRNA biomarkers is altered or modulated in the biological sample, the prognosis may be negative or positive.

In one embodiment, the prognosis is used, at least in part, to determine whether the subject would benefit from treatment of melanoma.

In one embodiment, the prognosis is used, at least in part, to develop a treatment strategy for the subject.

In one embodiment, the prognosis is used, at least in part, to determine disease progression in the subject.

In one embodiment, the prognosis is defined as an estimated time of survival.

In one embodiment, the method of this aspect further includes determining suitability of the subject for treatment based, at least in part, on the prognosis.

In one embodiment of the first and second aspects, the method further comprises determining a disease stage and/or grade for melanoma based on the expression level of the one or more miRNA biomarkers.

In one embodiment of the first and second aspects, the expression level of the one or more miRNA biomarkers is determined before, during and/or after treatment.

In a third aspect, the invention provides a method of treating melanoma in a subject including;

determining an expression level of one or more miRNA biomarkers in a biological sample from the subject, before, during and/or after treatment of melanoma, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-473, or a fragment or variant thereof, and based on the determination made, initiating, continuing, modifying or discontinuing a treatment of melanoma.

In a fourth aspect, the invention provides a method of evaluating treatment efficacy of melanoma in a subject including:

determining an expression level of one or more miRNA biomarkers in a biological sample from the subject before, during and/or after treatment, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, or a fragment or variant thereof; and

determining whether or not the treatment is efficacious according to whether said expression level of one or more miRNA biomarkers is altered or modulated in the subject's biological sample.

In one embodiment of the third and fourth aspects, the method further comprises selecting a treatment for melanoma based on the expression level of the miRNA biomarkers.

In particular embodiments, the method of the first, second, third and fourth aspects further comprises measuring an expression level of one or more additional miRNA biomarkers selected from the group consisting of miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p, or a fragment or variant thereof.

In particular embodiments of the method of the first, second, third and fourth aspects, an expression level of a protein or nucleic acid biomarker may also be determined. Suitably, the protein or nucleic acid biomarker is selected from the group consisting of serum lactate dehydrogenase (LDH), S-100B, tyrosinase, melanoma-inhibiting activity (MIA), tumour-associated antigen 90 immune complex (TA90IC), C-reactive protein, galectin-3, melanoma associated antigen, interleukin-8, matrix metalloprotease(MMP)-1, MMP-3 and YKL-40.

In particular embodiments of the method of the first, second, third and fourth aspects, the biological sample comprises tissue, blood, serum, plasma or cerebrospinal fluid. Typically, the miRNAs described herein are obtainable from a non-cellular source. Accordingly, the biological sample is, comprises, or is obtained from a non-cellular source. To this end, the biological sample may be serum, plasma, or cerebrospinal fluid, although without limitation thereto.

Suitably, the subject referred to herein is a mammal. Preferably, the subject is a human.

As used herein, except where the context requires otherwise, the term “comprise” and variations of the term, such as “comprising”, “comprises” and “comprised”, are not intended to exclude further elements, components, integers or steps but may include one or more unstated further elements, components, integers or steps.

It will be appreciated that the indefinite articles “a” and “an” are not to be read as singular indefinite articles or as otherwise excluding more than one or more than a single subject to which the indefinite article refers. For example, “a” cell includes one cell, one or more cells and a plurality of cells.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1. ‘Melanoma-specific’ miRNAs were first identified in a ‘Discovery set’ of 233 miRNAs. The ‘MELmiR-18’ panel was measured firstly in an independent cohort of FFPE melanoma tissues (‘Validation Cohort 1’). These data are summarised if Table 4. The ‘MELmiR-18’ panel was then measured (‘Validation Cohorts 2 and 3’) in two cohorts of serum derived from controls in comparison to AJCC staged melanoma patients (stages I-IV). These data are summarised in Table 2 and FIGS. 6 and 7. The ‘MELmiR-7’ panel was identified and carried forward in subsequent analysis as each miRNA member achieved high significance (P<0.0001) and an AUC score >0.70 in the ‘Validation Cohort 2 and 3’ comparison.

FIG. 2. The diagnostic decision tree summarises the utility of the ‘MELmiR-7’ panel in a diagnostic setting. The ‘MELmiR-7’ panel achieves very high sensitivity and specificity when a diagnostic score of 2-7 is attained (see Table 2 for further details).

FIG. 3. Predictions derived from Binary Logistic Regression (BLR) analysis are summarised. This represents the versatility of the ‘MELmiR-7’ panel for not only diagnosis of melanoma, but also for its predictive power for progression and recurrence of disease.

FIG. 4. Expression levels of miR-211-5p were assigned as high or normal expression based upon the comparison of stage IV melanoma with controls (FIG. 1). Using ROC analysis, high expression was determined if the median-normalised CT value was >85% sensitive. The Kaplan-Meier curve shows clear separation between high and normal miR-211-5p expressers median survival being 1.1 years vs. 3.5 years (HR 3.2, 95% CI 2.02-5.14, p<0.0001)

FIG. 5. Graphical representation of the data presented in Table 4 for stage III and IV FFPE tissues following a Mann-Whitney U test (unpaired). ns=no significance; *=P 0.05-0.01; **=P<0.01; ***=P<0.001; ****=P<0.0001.

FIG. 6. Graphical representation of the data presented in Table 5 for the MELmir-8 panel which were those that showed high significance (P<0.0001) between stage IV vs controls following a Mann-Whitney U test (unpaired). ns=no significance; *=P 0.05-0.01; **=P<0.01; ***=P<0.001; ****=P<0.0001.

FIG. 7. ROC curves derived from the data presented in Table 5 for the MELmir-8 panel which were those that showed high significance (P<0.0001) between stage IV vs controls. All members of the ‘MELmir-7’ panel have AUC scores>0.7. Using this cut-off, miR-204 is excluded.

FIG. 8. Unsupervised hierarchical cluster tree (Euclidean similarity with average linkage) of all miRNA genes and samples (Tables 7 and 8) Distinct separation can be observed for most melanoma (cutaneous) samples (brown) compared with other solid malignancies (green). Controls (melanocytes and melanoblasts) (red), uveal melanoma (UMM) (pink), melanoma patient-derived serum (blue) and nevocyte (grey) are also present in the tree.

FIG. 9. The top 3 upregulated miRNAs were validated using qRT-PCR with all samples present on the microarray along with an extended cohort of melanoma cell lines (Table 10). For comparative purposes the array data was also plotted to highlight the high correlation between the array and qRT-PCR.

FIG. 10. The nucleic acid sequence and observed sequence variation of miRNA-4487.

FIG. 11. The nucleic acid sequence and observed sequence variation of miRNA-4706.

FIG. 12. The nucleic acid sequence and observed sequence variation of miRNA-4731.

FIG. 13. The nucleic acid sequence and observed sequence variation of miRNA-16.

FIG. 14. The nucleic acid sequence and observed sequence variation of miRNA-211.

FIG. 15. The nucleic acid sequence and observed sequence variation of miRNA-509-3p and miRNA-509-5p.

BRIEF DESCRIPTION OF THE SEQUENCES

-   SEQ ID NO: 1=nucleic acid sequence miRNA-4487 of FIG. 10 (mature     sequence of miRNA-4487) -   SEQ ID NO: 2=nucleic acid sequence miRNA-4706 of FIG. 11 (mature     sequence of miRNA-4706) -   SEQ ID NO: 3=nucleic acid sequence miRNA-4731 of FIG. 12 (mature     sequence of miRNA-4731-5p) -   SEQ ID NO: 4=nucleic acid sequence miRNA-16 of FIG. 13 (mature     sequence of miRNA-16-5p) -   SEQ ID NO: 5=nucleic acid sequence miRNA-211 of FIG. 14 (mature     sequence of miRNA-211-5p) -   SEQ ID NO: 6=nucleic acid sequence miRNA-509-3p of FIG. 15 (mature     sequence of miRNA-509-3p) -   SEQ ID NO: 7=nucleic acid sequence miRNA-509-5p of FIG. 15 (mature     sequence of miRNA-509-5p)

DETAILED DESCRIPTION

The present invention is predicated, at least in part, on the surprising discovery that a novel panel of micro RNAs (miRNAs or miRs) may provide a serum-based prognostic and/or diagnostic biomarker for melanoma. In this regard, the inventors demonstrate herein that the panel may be used to monitor disease progression in melanoma patients, particularly in those patients with metastatic disease. Accordingly, the miRNAs disclosed herein may also have utility in methods of treating and/or evaluating treatment efficacy in melanoma patients.

In an aspect, the invention provides a method of determining whether or not a subject has melanoma, including:

determining an expression level of one or more miRNA biomarkers in a biological sample from a subject, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, and wherein melanoma is detected if said expression level of one or more miRNA biomarkers, is altered or modulated in the biological sample.

As generally used herein, the terms “cancer”, “tumour”, “malignant” and “malignancy” refer to diseases or conditions, or to cells or tissues associated with the diseases or conditions, characterized by aberrant or abnormal cell proliferation, differentiation and/or migration often accompanied by an aberrant or abnormal molecular phenotype that includes one or more genetic mutations or other genetic changes associated with oncogenesis, expression of tumour markers, loss of tumour suppressor expression or activity and/or aberrant or abnormal cell surface marker expression.

“Melanoma”, as used herein, refers to the malignant proliferation of melanocytes. The term “melanoma” is used, unless stated otherwise, in the broadest sense and refers to all stages and all forms of the cancer, such as malignant, metastatic (regional or distant), cutaneous, uveal and recurrent melanoma. As would be understood by the skilled artisan, this includes those subjects with melanoma that may be asymptomatic and/or a diagnosis of melanoma has not been provided or is ambiguous and yet the subject does indeed have melanoma.

The term “determining” includes any form of measurement, and includes determining if an element is present or not. As used herein, the terms “determining”, “measuring”, “evaluating”, “assessing” and “assaying” are used interchangeably and include quantitative and qualitative determinations. Determining may be relative or absolute. “Determining the presence of” includes determining the amount of something present (e.g., an miRNA and/or protein biomarker), and/or determining whether it is present or absent.

As would be understood by the skilled person, the expression level of the one or more miRNA biomarkers is deemed to be “altered” or “modulated” when the amount or expression level of the respective miRNA biomarker is increased or up regulated or decreased or down regulated, as defined herein.

In one embodiment, melanoma is detected if the one or more miRNA biomarkers are at a reduced level, down regulated or absent in the biological sample. In an alternative embodiment, melanoma is detected if the one or more miRNA biomarkers are at an increased level, up regulated or present in the biological sample.

By “enhanced”, “increased” or “up regulated” as used herein to describe the expression level of miRNA, protein and/or nucleic acid biomarkers, refers to the increase in and/or amount or level of one or more miRNA, protein and/or nucleic acid biomarkers, including variants, in a biological sample when compared to a control or reference sample or a further biological sample from a subject. The expression level of a biomarker may be relative or absolute. In some embodiments, the expression level of the one or more miRNA, protein and/or nucleic acid biomarkers is increased if its level of expression is more than about 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 150%, 200%, 300%, 400% or at least about 500% higher than the level of expression of the corresponding miRNA, protein and/or nucleic acid biomarkers in a control sample or further biological sample from a subject.

The terms, “reduced” and “down regulated”, as used herein to describe the expression level of miRNA, protein and/or nucleic acid biomarkers, refer to a reduction in and/or amount or level of one or more miRNA, protein and/or nucleic acid biomarkers, including variants, in a biological sample when compared to a control or reference sample or further biological sample from a subject. The expression level of a biomarker may be relative or absolute. In some embodiments, the expression level of one or more miRNA, protein and/or nucleic acid biomarkers is reduced or down regulated if its level of expression is more than about 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20% or 10%, or even less than about 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001% of the level of expression of the corresponding miRNA, protein and/or nucleic acid biomarkers in a control sample or further biological sample from a subject.

The term “control sample” typically refers to a biological sample from a healthy or non-diseased individual not having melanoma. In one embodiment, the control sample may be from a subject known to be free of melanoma. Alternatively, the control sample may be from a subject in remission from melanoma. The control sample may be a pooled, average or an individual sample. An internal control is a marker from the same biological sample being tested.

Suitably, the expression level of any combination of the miRNA biomarkers miRNA-4487, miRNA-4706 and miRNA-4731 in a biological sample may be determined, including for example: miRNA-4487 and miRNA-4706; miRNA-4487 and miRNA-4731; miRNA-4706 and miRNA-4731; or miRNA-4487, miRNA-4706 and miRNA-4731.

In one embodiment the method of determining whether or not a subject has melanoma includes determining the expression level of a further miRNA biomarker in addition to miRNA-4487, miRNA-4706 and miRNA-4731 in a biological sample from the subject, wherein the miRNA biomarkers are selected from the following group: miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p, and wherein melanoma may be detected if at least one of the miRNA biomarkers is at an altered or modulated level or expression in the biological sample when compared to a control or reference sample. By way of example, an increase in the level or up regulation in the biological sample of a further biomarker, such as miRNA-16 and/or miRNA-211, may indicate the presence of melanoma in the subject. Further, a decrease in the level, down regulation or absence in the biological sample of a further biomarker, such as miRNA-509-3p and/or miRNA-509-5p, may indicate the presence of melanoma in the subject.

It will be appreciated that the methods of the invention include methods of determining the expression level of the one or more miRNA biomarkers alone or in combination with one or more protein and/or nucleic acid biomarkers which have been identified as being diagnostic for melanoma. In certain embodiments, the expression level of one or more protein and/or nucleic acid biomarkers may also be determined. In this regard, the one or more protein and/or nucleic acid biomarkers may include, for example, serum lactate dehydrogenase (LDH), S-100B, tyrosinase, VEGF, bFGF, ICAM-1, matrix metalloproteases (MMPs; e.g., MMP-1 and MMP-3), cytokines and/or their receptors (e.g., IL-6, IL-8, IL-10), HLA molecules, TuM2-PK, apoptosis markers (e.g., Fas/CD95) karyopherin-alpha2, MCMs (minichromosome maintenance proteins), melanoma-inhibiting activity (MIA), tumour-associated antigen 90 immune complex (TA90IC), C-reactive protein, galectin-3, melanoma associated antigens (e.g., MAGE proteins), YKL-40, geminin and PCNA, albeit without limitation thereto. Additional diagnostic and prognostic protein and nucleic acid biomarkers for melanoma have been described previously, as reviewed in DIAGNOSTIC AND PROGNOSTIC BIOMARKERS AND THERAPEUTIC TARGETS IN MELANOMA Ed. Murphy, M. J., (Humana Press, 2012), which is incorporated by reference herein.

The expression level of the one or more miRNA, protein and/or nucleic acid biomarkers may be determined by any method known in the art. By way of example, the expression level of miRNA biomarkers may be determined by hybridization-based techniques (e.g., Northern blots, in situ hybridization, RT-PCR, and microarrays), amplification-based techniques (e.g., real-time quantitative PCR; gold nanoparticle-initiated silver enhancement) and cloning-based techniques (e.g., miRAGE).

Suitably, when the expression level of the one or more miRNA biomarkers and/or one or more protein and/or nucleic acid biomarkers are determined, they can be derived from the same or different samples. For example, the expression level of the one or more miRNA biomarkers can be determined in a blood derived sample and the expression level of a protein biomarker can be determined in a tissue sample.

In particular embodiments, the biological sample comprises tissue, blood, serum, plasma or cerebrospinal fluid. Typically, the miRNAs described herein are obtainable from a non-cellular source. Accordingly, the biological sample is, comprises, or is obtained from a non-cellular source. To this end, the biological sample may be serum, plasma, or cerebrospinal fluid, although without limitation thereto.

In some embodiments, the method of determining whether or not a subject has melanoma may be performed in “high throughput” diagnostic tests or procedures such as performed by commercial pathology laboratories or in hospitals. Furthermore or alternatively, the method of the present aspect may be used to confirm a diagnosis of melanoma, including metastatic and recurrent melanoma, such as that initially detected by a different or alternative diagnostic test or procedure.

It would be further appreciated, that such methods of determining miRNA expression in the biological sample from a melanoma patient may have potential utility in characterising disease progression and/or severity of a given patient. Additionally, such methods may be used for selecting patients for particular treatments.

In one embodiment, the method determines whether the subject has metastatic melanoma.

As used herein, “metastasis” or “metastatic”, refers to the migration or transfer of malignant tumour cells, or neoplasms, via the circulatory or lymphatic systems or via natural body cavities, typically from the primary focus of tumour, cancer or a neoplasia to a distant site in the body, and the subsequent development of one or more secondary tumours or colonies thereof in the one or more new locations. “Metastases” refers to the secondary tumours or colonies formed as a result of metastasis and encompasses micro-metastases as well as regional and distant metastases.

In one embodiment, the method determines whether the subject has recurrent melanoma.

In another aspect, the invention provides a method of determining the prognosis of a subject with melanoma, including:

determining an expression level of one or more miRNA biomarkers in a biological sample obtained from the subject, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, to thereby evaluate the prognosis of melanoma in the subject.

Suitably, if the expression level of said one or more miRNA biomarkers, is altered or modulated in the biological sample, the prognosis may be negative or positive.

The terms “prognosis” and “prognostic” are used herein to include making a prognosis, which can provide for predicting a clinical outcome (with or without medical treatment), selecting an appropriate course of treatment (or whether treatment would be effective) and/or monitoring a current treatment and potentially changing the treatment. This may be at least partly based on determining expression levels of one or more miRNA biomarkers by the methods of the invention, which may be in combination with determining the expression levels of additional protein and/or other nucleic acid biomarkers. A prognosis may also include a prediction, forecast or anticipation of any lasting or permanent physical or psychological effects of melanoma suffered by the subject after the melanoma has been successfully treated or otherwise resolved. Furthermore, prognosis may include one or more of determining metastatic potential or occurrence, therapeutic responsiveness, implementing appropriate treatment regimes, determining the probability, likelihood or potential for melanoma recurrence after therapy and prediction of development of resistance to established therapies (e.g., chemotherapy). It would be appreciated that a positive prognosis typically refers to a beneficial clinical outcome or outlook, such as long-term survival without recurrence of the subject's melanoma, whereas a negative prognosis typically refers to a negative clinical outcome or outlook, such as cancer recurrence or progression.

“Resistance” as used herein refers to a diminished or failed response of an organism, disease, tissue or cell to the intended effectiveness of a treatment, such as a chemical or drug. Resistance to a treatment can be already present at diagnosis or the start of treatment (i.e., intrinsic resistance) or it can develop with or after treatment (i.e., acquired resistance).

As described herein, the expression levels of miRNA-4487, miRNA-4706 and miR-4731 are typically decreased or down regulated upon the initial development of melanoma in a subject. The expression levels of these miRNAs, however, may be shown to be altered or modulated (i.e., they may increase and/or decrease) with disease progression, such as progression from Stage I/II to Stage III or Stage IV melanoma and from Stage III to Stage IV melanoma, despite typically remaining decreased or down regulated when compared to a control sample or level of expression.

In this regard, an increase or up regulation of the level in the biological sample of miRNA-4487 in a subject with Stage I/II or Stage III melanoma, may indicate a positive prognosis for the subject, such as disease remission or regression, or alternatively it may indicate a negative prognosis for the subject, such as disease progression to Stage IV melanoma. Further, a decrease or down regulation of the level in the biological sample of miRNA-4487 in a subject with Stage I/II melanoma may indicate a negative prognosis for the subject, such as disease progression to Stage III melanoma.

An increase or up regulation of the level in the biological sample of miRNA-4706 in a subject with Stage I/II or Stage III melanoma, may indicate a positive prognosis for the subject, such as disease regression or remission, or alternatively it may indicate a negative prognosis for the subject, such as disease progression to Stage III or Stage IV melanoma.

An increase or up regulation of the level in the biological sample of miRNA-4731 in a subject with Stage I/II melanoma, may indicate a positive prognosis for the subject, such as disease regression or remission, or alternatively it may indicate a negative prognosis for the subject, such as disease progression to Stage III melanoma. Further, a decrease or down regulation of the level in the biological sample of miRNA-4731 in a subject with Stage III melanoma may indicate a negative prognosis for the subject, such as disease progression to Stage IV melanoma. Additionally, an increase or up regulation of the level in the biological sample of miRNA-4731 in a subject with Stage III or IV melanoma, may indicate a positive prognosis for the subject, such as disease regression or remission.

In one embodiment, the method further comprises measuring an expression level of one or more additional miRNA biomarkers selected from the group consisting of miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p.

In this regard, an increase or up regulation of the level in the biological sample of an additional biomarker, such as miRNA-16 and/or miRNA-211, may indicate a negative prognosis for the subject. Further, a decrease or down regulation of the level in the biological sample of an additional biomarker, such as miRNA-509-3p and/or miRNA-509-5p, may indicate a negative prognosis for the subject. Additionally, a decrease or down regulation of the level in the biological sample of miRNA-16 in a subject with Stage III melanoma, may indicate a positive prognosis for the subject, such as disease regression or remission, or alternatively it may indicate a negative prognosis for the subject, such as disease progression to Stage IV melanoma.

In certain embodiments, the expression level of one or more protein and/or nucleic acid biomarkers, as hereinbefore described, may also be determined.

In particular embodiments, the biological sample comprises tissue, blood, serum, plasma or cerebrospinal fluid. Preferably, the biological sample comprises serum, plasma, or cerebrospinal fluid, although without limitation thereto.

In one embodiment, the method further comprises determining a disease stage and/or grade for the subject's melanoma based on the expression level of the one or more miRNA biomarkers. In this regard, the method may be used to determine whether the subject's melanoma has metastasized regionally, such as to lymph nodes (i.e., Stage III melanoma), and/or distantly to other parts of the subject's body (i.e., Stage IV melanoma).

In one embodiment, the expression level of the one or more miRNA biomarkers is determined before, during and/or after treatment.

In one embodiment, the prognosis is used, at least in part, to determine whether the subject would benefit from treatment of the melanoma.

In one embodiment, the prognosis is used, at least in part, to develop a treatment strategy for the subject.

In one embodiment, the prognosis is used, at least in part, to determine disease progression or recurrence in the subject.

In one embodiment, the prognosis is defined as an estimated time of survival.

In one embodiment, the method of this aspect further includes determining suitability of the subject for treatment based, at least in part, on the prognosis.

In a further aspect, the invention provides a method of treating melanoma in a subject including;

determining an expression level of one or more miRNA biomarkers in a biological sample from a subject, before, during and/or after treatment of melanoma, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706, miRNA-4731 and based on the determination made, initiating, continuing, modifying or discontinuing a treatment of melanoma.

As used herein, “treating”, “treat” or “treatment” refers to a therapeutic intervention, course of action or protocol that at least ameliorates a symptom of melanoma after the cancer and/or its symptoms have at least started to develop. As used herein, “preventing”, “prevent” or “prevention” refers to therapeutic intervention, course of action or protocol initiated prior to the onset of cancer and/or a symptom of cancer so as to prevent, inhibit or delay development or progression of the cancer or the symptom.

In one embodiment, the method further comprises measuring an expression level of one or more additional miRNA biomarkers selected from the group consisting of miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p.

In certain embodiments, the expression level of one or more protein and/or nucleic acid biomarkers, as herein before described, may also be determined.

In particular embodiments, the biological sample comprises tissue, blood, serum, plasma or cerebrospinal fluid. Preferably, the biological sample comprises serum, plasma, or cerebrospinal fluid, although without limitation thereto.

In one embodiment, the method further comprises selecting a treatment for melanoma based on the expression level of the miRNA biomarkers.

It will be appreciated that the method of treating melanoma may include administration of one or more other therapeutic agents that facilitate melanoma treatment or prevention. By way of example only, these include: chemotherapeutic agents such as paclitaxel, doxorubicin, methotrexate, irinotecan, dacarbazine, temozolomide and cisplatin, although without limitation thereto; biotherapeutic agents such as anti-PD-1 antibodies (e.g., Nivolumab) and anti-CTLA4 antibodies (e.g., Ipilimumab), although without limitation thereto; and/or molecularly targeted agents such as MAPK pathway (i.e., RAS-RAF-MEK-ERK signalling) inhibitors and phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway inhibitors, although without limitation thereto.

In one embodiment, the method further comprises selecting a treatment for melanoma based on the expression level of the miRNA biomarkers.

In yet another aspect, the invention provides a method of evaluating treatment efficacy of melanoma in a subject including;

determining an expression level of one or more miRNA biomarkers in a biological sample from the subject before, during and/or after treatment, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731; and

determining whether or not the treatment is efficacious according to whether said expression level of one or more miRNA biomarkers is altered or modulated in the subject's biological sample.

As noted earlier, the expression levels of miRNA-4487, miRNA-4706 and miRNA-4731, may increase and/or decrease with disease progression, despite typically remaining decreased or down regulated when compared to a control sample.

In this regard, an increase or up regulation of the level in the biological sample of miRNA-4487 in a subject with Stage I/II or Stage III melanoma undergoing treatment, may indicate disease remission or regression in the subject, and as such the treatment is efficacious, or alternatively it may indicate disease progression to Stage IV melanoma and the treatment is inefficacious. Further, a decrease or down regulation of the level in the biological sample of miRNA-4487 in a subject with Stage I/II melanoma undergoing treatment may indicate disease progression to Stage III melanoma and the treatment is inefficacious.

An increase or up regulation of the level in the biological sample of miRNA-4706 in a subject with Stage I/II or Stage III melanoma undergoing treatment, may indicate disease regression or remission and the treatment is efficacious, or alternatively it may indicate disease progression to Stage III or Stage IV melanoma and the treatment is inefficacious.

An increase or up regulation of the level in the biological sample of miRNA-4731 in a subject with Stage I/II melanoma undergoing treatment, may indicate disease regression or remission and the treatment is efficacious, or alternatively it may indicate disease progression to Stage III melanoma and the treatment is inefficacious. Further, a decrease or down regulation of the level in the biological sample of miRNA-4731 in a subject with Stage III melanoma undergoing treatment may indicate disease progression to Stage IV melanoma and the treatment is inefficacious. Additionally, an increase or up regulation of the level in the biological sample of miRNA-4731 in a subject with Stage III or IV melanoma undergoing treatment, may indicate disease regression or remission and the treatment is efficacious.

In one embodiment, the method further comprises measuring the expression level of one or more additional miRNA biomarkers selected from the group consisting of miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p. In this regard, a decrease or down regulation of the level in the biological sample of an additional biomarker, such as miRNA-16 and/or miRNA-211, may indicate the treatment is efficacious. Additionally, for miRNA-16, a decrease or down regulation of its expression level in the biological sample from a subject with Stage I/II or III melanoma, may indicate disease progression to Stage IV melanoma and the treatment is inefficacious. Further, an increase in the level or up regulation in the biological sample of an additional biomarker, such as miRNA-509-3p and/or miRNA-509-5p, may indicate the treatment is efficacious.

In certain embodiments, the expression level of one or more protein and/or nucleic acid biomarkers, as herein before described, may also be determined.

In particular embodiments, the biological sample comprises tissue, blood, serum, plasma or cerebrospinal fluid. Preferably, the biological sample comprises serum, plasma, or cerebrospinal fluid, although without limitation thereto.

It would be appreciated by the skilled artisan that the methods of present invention also include within their scope fragments and variants of the miRNA biomarkers described herein.

In this regard, a miRNA “fragment” includes a nucleic acid sequence that constitutes less than 100%, but at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of said miRNA sequence. In particular embodiments, a miRNA fragment may comprise, for example, at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and 24 contiguous nucleotides of said miRNA biomarkers.

As used herein, “variant” refers to those miRNAs described herein that have one or more nucleic acids deleted, added or substituted by different nucleotides. In particular embodiments, a miRNA variant may comprise, for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9 nucleotide deletions, additions and/or substitutions. In this regard, it is well known in the art that miRNAs can demonstrate some degree of variation from their respective reference miRNA sequence. By way of example and referring to FIGS. 11-15, it will be appreciated that the nucleotide sequences of SEQ ID NOS: 1-7 are “canonical” or preferred sequences and that there may be significant variability with respect to the length of the nucleotide sequence of each miRNA biomarker, in particular.

In some embodiments, a diagnostic, prognostic and/or treatment expression level of the one or more miRNA biomarkers described herein is correlated to melanoma by merely its presence or absence. In other embodiments, a threshold level of a diagnostic, prognostic and or treatment expression level of the one or more miRNA biomarkers can be established, and the level of the one or more miRNA biomarkers in a subject's biological sample can simply be compared to the threshold level.

In some embodiments, multiple time points prior to, during and/or after treatment of a subject with melanoma may be selected to determine the expression level of the one or more miRNA biomarkers, with or without other biomarkers, to determine a diagnosis, prognosis or treatment efficacy. For example, the expression level of the one or more miRNA biomarkers with or without additional specific miRNA biomarkers, protein biomarkers and/or nucleic acid biomarkers can be determined at an initial time point and then again at one, two, three or more time points.

Suitably, the time points may be selected throughout a treatment cycle or over a desired time period. Over a desired time period, for example, the time points may be prior to treatment, mid way through treatment and/or after treatment has been completed. Suitably, an altered or modulated expression level, such as a decrease or reduction in the expression level, of the one or more miRNA biomarkers, such as miRNA-4487, miRNA-4706 and/or miRNA-4731, may be utilised by the methods of the invention from the first to second and/or third time points may provide a poor prognosis for a subject with melanoma. Alternatively, an altered or modulated expression level, such as an increase or upregulation in the expression level, of the one or more miRNA biomarkers, such as miRNA-4487, miRNA-4706 and/or miRNA-4731, utilised by the methods of the invention from the first to second and/or third time points may provide a positive prognosis for a subject with melanoma.

As would be appreciated by the skilled artisan, the alteration or modulation in expression level of the one or more miRNA biomarkers may also be related to the severity, stage, recurrence or progression of melanoma and/or the efficacy of the treatment. By way of example, differences in the expression levels of the one or more miRNA biomarkers may be used to delineate stage III (i.e., regional metastatic) from stage IV (i.e., distant metastatic) melanoma patients. In addition, differences in the expression levels of the one or more miRNA biomarkers may be used to delineate stage I/II (i.e., non-metastatic) from stage III/IV (i.e., metastatic) melanoma patients.

In one embodiment, biological samples may be sourced and/or collected from a subject at diagnosis and then prior to each cycle of treatment. Suitably, there may be any number of treatment cycles, depending on the subject and the nature and/or stage of melanoma, including but not limited to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 and/or 20 cycles. The treatment cycles may be close together, spread out over a period of time and/or intense cycles at defined time points over a period of time, or any combination of the above.

In further embodiments, samples may be taken both during treatment and/or after treatment has been completed. Suitably, samples may be sourced from a subject at any time point after treatment has been completed, examples of which include 1, 2, 3, 4, 5, 10, 15, 20, 25 and/or 30 days post treatment, 1, 2 and/or 3 weeks post treatment and/or 1, 3, 6 and/or 9 months post treatment and/or 1, 2, 3, 4, 5, 10, 15, 20 and/or 30 years post treatment. The treatment may be completed once the subject is in remission or after at least one or more treatment cycles, depending on the subject and the melanoma.

In some embodiments, subjects are sampled every three to six months and/or every year post treatment. It will be understood by a person of skill in the art that a subject may be in remission or may have non-responsive or relapsed melanoma. If subjects have non-responsive or relapsed disease, then monitoring may be more frequent.

In one embodiment, a decrease or no change in the expression level of the one or more miRNA biomarkers, such as miRNA-4487, miRNA-4706 and/or miRNA-4731, in a second, third, fourth and/or fifth etc., biological sample from a subject collected either after treatment or during treatment as compared to the expression level in a first earlier sample, may indicate progression of melanoma or failure of the treatment, such as the presence and/or development of resistance.

In one embodiment, an increase in the expression level of the one or more miRNA biomarkers, such as miRNA-4487, miRNA-4706 and/or miRNA-4731, in a second, third, fourth and/or fifth etc., biological sample from a subject collected either after treatment or during treatment as compared to the expression level in a first earlier sample, may indicate that the treatment is efficacious.

The methods described herein may be suitable for any biological sample from a subject. In particular embodiments, the biological sample is or comprises tissue, blood, serum, plasma or cerebrospinal fluid. Typically, the miRNAs are obtainable from a non-cellular source. Accordingly, the biological sample is, comprises, or is obtained from a non-cellular source. The biological sample may be serum, plasma, or cerebrospinal fluid, although without limitation thereto.

Additionally, the one or more biomarkers described herein may be used in in vitro assays of response by subject-derived test cells or tissue to predict an in vivo response to the treatment. As such, the one or more biomarkers described herein, may be used, for example, to predict and monitor how particular subjects with melanoma respond to therapeutic intervention with the treatment. A biomarker expression pattern correlating with sensitivity or resistance of cells following exposure of the cells to the treatment may provide a useful tool for screening one or more tumour samples from the subject before treatment. The screening allows a prediction of cells of a biological sample of the melanoma exposed to the treatment, based on the expression results of the one or more biomarkers, as to whether or not the melanoma, and hence a patient harbouring the melanoma, will or will not respond to the treatment.

It would be understood that the methods described herein may be applicable to any mammal. In particular embodiments, the term “mammal” includes but is not limited to humans, performance animals (such as horses, camels, greyhounds), livestock (such as cows, sheep, horses) and companion animals (such as cats and dogs). Preferably, the subject is a human.

So that the present invention may be more readily understood and put into practical effect, the skilled person is referred to the following non-limiting examples.

EXAMPLE 1 Materials and Methods

Patient Specimen Details and Inclusion Criteria

VALIDATION Cohort 1. Stage III melanoma tissues (‘PAH-tissue’) were obtained from a prospective database of stage IIIA-C cutaneous melanoma patients, presenting to the Princess Alexandra Hospital (PAH) Melanoma Unit and affiliated private hospitals, which has been maintained since 1997. Permission to collect and use information was approved by the hospital ethics committee (HREX Reference number: HREX/11/QPAH/650; SSA reference number: SSA/11/QPAH/694). Formalin-fixed paraffin embedded (FFPE) tumour samples from 72 patients who met these criteria were reviewed according to the AJCC staging system for cutaneous melanoma seventh edition¹ by an experienced melanoma pathologist. Pathologic and treatment-related variables were prospectively recorded.

Stage III and IV melanoma tissues ‘MIA-tissue’ were obtained from a prospective database of melanoma patients, presenting to the Melanoma Institute Australia (formerly Sydney Melanoma Unit) and affiliated private hospitals, which has been maintained since 1967. Cases and corresponding available FFPE tissue specimens were selected that fell into one of the following three categories, (1) diagnosed with distant metastatic melanoma and survived less than 1 year, (2) diagnosed with distant metastatic melanoma and survived greater than 5 years, and (3) diagnosed with regional lymph node metastasis. Informed written consent was obtained for each patient under approved protocols (Protocol No X10-0305 &HREC/10/RPAH/539 and Protocol No X10-0300 HREC/10/RPAH/530) governed by the Human Research Ethics Committee of the Royal Prince Alfred Hospital (Sydney NSW, Australia)

VALIDATION Cohort 2 and 3. Sera from ‘Healthy Controls’ were ascertained from a large cohort of participants collected as part of the Australian Cancer Study (ACS) (QIMR Berghofer HREC approved project no. P399). As part of the ACS, potential controls were randomly selected from the Australian Electoral Roll (enrollment is compulsory). Controls were prospectively sampled from within strata of age (in 5 year age-groups) and state of residence. Of 3,258 potentially eligible control participants, 41 could not be contacted and 175 were excluded because they were deceased (16), too ill (61), or unable to read or write in English (98). Of 3,042 controls meeting the inclusion criteria, 1680 (55%) gave their consent to take part. Completed questionnaires were returned by 1580 controls (48% of all potentially eligible controls selected from the roll). See Table 1 for participant descriptive statistics.

Sera from ‘High nevus count’ and ‘History of melanoma’ participants were prospectively collected from cohorts who were enrolled in a large study titled: ‘Pigmentation genotypes and phenotypic correlations with dermoscopic naevus types and distribution’. These samples were included as ‘controls’ to determine the level of expression measurable in sera derived from patients with a high melanocyte burden. All study participants were enrolled in the following human ethics approved projects: QIMR HREC/P1237, The Metro South Health District HREC/09/QPAH/162, and UQ HREC approval number is 2009001590. Participants with a history of melanoma were recruited through the Melanoma Unit and Dermatology Department of the Princess Alexandra Hospital, Brisbane, Queensland, Australia, between May 2012 and November 2012. Control participants, with no personal history of melanoma, were recruited from the Brisbane Twin Naevus Study between August 2012 and November 2012. All participants had 16-panel full-body images and dermoscopic images of significant naevi recorded²¹. Significant naevi were defined as naevi greater than or equal to 5 mm on all body sites except the scalp, buttocks, mucosal surfaces and genitals, and greater than or equal to 2 mm on the back of both males and females and on the legs of females. All significant naevi were classified by the predominant dermoscopic pattern (reticular, globular, or non-specific), colour, and profile (flat, raised, domed or papillomatous). See Table 1 for participant descriptive statistics.

Sera from Stage I/II and IV melanoma patients (staged according to the current AJCC staging manual¹) had blood drawn and serum stored as part of a large prospectively collected cohort from the university department of dermatology in Tubingen, Germany (‘Tubingen’ cohort). Bio-banking and the usage of corresponding patient data for this study was approved by the Ethics Committee, University of Tubingen (approvals 657/2012BO2). An additional cohort of patients was obtained from the Melanoma Institute of Australia, Sydney (‘MIA’ cohort). Patients were staged I-IV according to the current AJCC staging guidelines¹. Informed written consent was obtained for each patient under approved protocols (Protocol No X10-0305 &HREC/10/RPAH/539 and Protocol No X10-0300 HREC/10/RPAH/530) governed by the Human Research Ethics Committee of the Royal Prince Alfred Hospital (Sydney NSW, Australia).

Total RNA extraction from ‘Validation cohort 1’: FFPE tissue. The extraction of total RNA from FFPE tissue was performed using miRNeasy FFPE Kits (QIAGEN). A sterile disposable biopsy punch (BPP-20F; Kai Medical, Japan) was used to retrieve tumour content from blocks that had been scored and marked for content via H&E histological staining. This process allowed for minimal stromal contamination. This kit is specifically designed to reverse the formalin crosslinking of RNA, efficiently releasing RNA from tissue sections, while avoiding significant RNA degradation.

Total RNA extraction from ‘Validation cohorts 2 and 3’: serum. Total RNA was extracted from serum following the manufacturer's protocol using the miRNeasy Serum/Plasma Kits (QIAGEN) which efficiently purify RNA from up to 200 μl serum or plasma. As there is currently no consensus as to which miRNA should be used as an endogenous control¹⁵, a synthetic miRNA mimic (miRNeasy Serum/Plasma Spike-In Control: C. elegans miR-39 miRNA mimic) was spiked into each sample (5.6×10⁸ copies/200 μl) to allow for normalization of expression data.

Selection criteria for ‘melanoma-specific’ miRNAs. In our previously published miRNA microarray data²⁰ we found a total of 233/1898 miRNAs (‘Discovery’ set) (FIG. 1) that were differentially expressed (DE; P≦0.05 and ≧2 fold) between a group of cutaneous melanoma cell lines (n=55) as compared to a group of other (non-melanoma) solid malignancies (n=34). We then applied filtering criteria to the 233 DE miRNAs to identify which miRNAs would be suitable to measure in patient derived serum. The following strict criteria were used to filter the ‘Discovery’ set: ≧15 fold higher expression in cutaneous melanoma vs ‘other’ solid malignancies (n=14/14), or ≧2 fold higher expression in cutaneous melanoma vs ‘other’ solid malignancies with no detectable expression in melanocytes or melanoblasts (n=3/6). In addition, miR-16, which is known to be highly expressed in blood, was also included as a control. This identified an 18 miRNA panel (‘MELmiR-18’) that consisted of: miR-211-5p, miR-514a-3p, miR-509-3p, miR-204-5p, miR-509-5p, miR-513b, miR-145-5p, miR-146a-5p, miR-508-3p, miR-506-3p, miR-513c-5p, miR-4731-5p, miR-508-5p, miR-363-3p, miR-4487, miR-4469, miR-4706, and hsa-miR-16. This panel was carried forward for validation in independent cohorts of patient derived sera and subsequent correlation with panel analysis in FFPE of melanoma tumours.

Reverse transcription and PreAmplification using custom primer and assay pools. The standard TaqMan® MicroRNA Assay protocol calls for an individual gene specific reverse transcription (RT) reaction for each assay, however in order to maximize the full capability of the Biomark HD (Fluidigm) system, we developed a modified protocol (User bulletin publication part number 4465407, revision date January 2013 (Rev. C)). Briefly, a custom RT primer pool consisting of equal amounts of miRNA-specific RT primer plus an additional pool of the corresponding TaqMan® MicroRNA Assay (PreAmp Primer Pool) were used to pre-amplify the RT reaction. The TaqMan® PreAmp Master Mix was used to amplify small amounts of cDNA reactions (this was specifically designed to avoid amplification bias). The RT protocol was as per manufacturers guidelines and the PreAmplification protocol was modified in the following way: total reaction volume was scaled down to 10 μL (2.5 μL of RT-product, 5 μL of TaqMan PreAmp Master Mix (2×), and 1.5 μL of PreAmp Primer Pool) with a total of 14 cycles used in the PreAmplification.

qRT-PCR using the Fluidigm® 96.96 Dynamic Array™ and BioMark™ HD. The 96.96 Dynamic™ Array on the Fluidigm system allows simultaneous measurement of 96 assays with 96 samples totaling 9216 reactions with minimal cDNA and (2.7 ul) assay (2.5 ul) input. The Dynamic Array has an on-chip network of microfluidic channels, chambers and valves that automatically assemble individual PCR reactions. The BioMark system has the ability to detect a single copy in a 6.75 nl reaction volume, a feat not possible with conventional real-time PCR instruments (5 ul minimum volume). In this way, it is possible to combine up to 96 primer assays to create a high throughput, specific and highly sensitive custom miRNA expression panel, which maximizes the information that can be obtained from each RNA sample. This ultra-sensitivity allows for confidence in its ability to detect subtle changes in gene expression—a requirement for serum miRNA detection.

qRT-PCR analysis to determine the median normalized Ct expression value. The expression of the ‘MELmiR-18’ panel (FIG. 1) was assayed in each sample with at least 4 replicate Taqman assays to determine their expression. Firstly, real-time expression data was analyzed and extracted using Fluidigm's Real-Time PCR Analysis software using default parameters (Quality Threshold=0.65, Baseline Correction Method=Linear, and Ct Threshold Method=Auto (Global)). The combined data from all 96.96 Dynamic Array runs were then combined in Microsoft Excel so as to perform global normalization (median normalized Ct value method)²². Firstly, the average Ct value was determined for each sample-assay combination (including the synthetic spike-in control cel-miR-39). The normalization factor was then derived by subtracting the mean cel-miR-39 Ct value of each sample from median cel-miR-39 Ct value of all samples. The median normalized Ct value was then calculated by adding the normalization factor to the averaged Ct value of each sample-assay combination.

For the FFPE samples, again, the average Ct value was determined for each sample-assay combination (including the endogenous control RNU-6). The normalization factor was then derived by subtracting the mean RNU-6 Ct value of a positive control sample with known quantity (15 ng) of total RNA (added to the initial cDNA reaction) from each RNU-6 assay-sample combination. The median normalized Ct value was then calculated by adding the normalization factor to the averaged Ct value of each sample-assay combination.

Statistical Analysis. All data analysis (Mann-Whitney U test, ROC analysis, and Binary Logistic Regression) was performed in Graph Pad Prism 6 or SPSS for Windows, version 21.0 (Statistical Package for the Social Sciences, SPSS, Inc., Chicago, Ill.) with median-normalized Ct expression values using default parameters.

Diagnostic inclusion criteria. To maximize the chances of having a positive signal expressed in the patients serum, only the combined stage IV cohorts were used (n=119; ‘TUBINGEN’ and ‘MIA’) to compare against disease-free ‘controls’ (n=130; no history of melanoma or nevi, prior history of melanoma, high nevus count with no melanoma). Initially, all members of the ‘MELmiR-18’ panel underwent a simple Mann-Whitney U test to identify the highly significant (p.<0.0001) miRNAs to be included in the next step (FIG. 1). Those miRNAs that met these criteria then underwent ROC analysis to determine their Area under the Curve (AUC) (FIGS. 1 and 2). The miRNAs that had an AUC≧0.70 (‘MELmiR-7’ panel) were interrogated further to classify the median-normalized Ct values as ‘high’ or ‘low’ expression for diagnostic test purposes along with survival analysis. AUC scores≧0.7 were deemed to be diagnostically useful²³. Interpretation of Ct expression values used to determine ROC curves were evaluated with an arbitrary cut-off of ‘85% sensitivity’. Those that were greater than this cut-off were classed as ‘high’ and those that were below were ‘low’. Binary Logistic Regression (SPSS for Windows, version 21.0 (Statistical Package for the Social Sciences, SPSS, Inc., Chicago, Ill.) analysis was also performed to provide further justification of the inclusion or exclusion of particular miRNAs in the MELmiR-7 panel.

Diagnostic Score assignment. For those miRNAs that met the criteria for inclusion in the diagnostic panel (FIGS. 1 and 2), the patient was given a diagnostic score (ranging from 0-7) determined by the number of miRNAs that were present as ‘high’ and ‘low’ (graphing of Ct values as part of the Mann-Whitney U test allowed a visual determinant of the stage IV cohort being higher or lower than the ‘control’ cohort for each miRNA) or ‘normal’ (most like the ‘control’ cohort). To be deemed positive for melanoma, the patients sample must have had a score≧2 (max 7). A negative test was a score of 0 or 1.

Diagnostic test evaluation. The following formulas were used to determine diagnostic test ability:

Positive Predictive Value (PPV) or Precision=True Positive (TP)/(TP+False Positive (FP))

Negative Predictive Value (NPV)=True Negative (TN)/(False Negative (FN)+TN)

Sensitivity=TP/(TP+FN)

Specificity=TN/(FP+TN)

False Positive Rate=1−Specificity

False Negative Rate=1−Sensitivity

Power=Sensitivity

Likelihood Ratio Positive=Sensitivity/1−Specificity

Likelihood Ratio Negative=1−Sensitivity/Specificity

Diagnostic Odds Ratio (DOR)=(TP/FN)/(FP/TN)

Survival Curve analysis. All Kaplan Meier curves were drawn in Graph Pad Prism 6 using date of last follow-up to compare to date of diagnosis (stage IV) to determine survival years and Alive/Dead status. ‘High’ and ‘Low’ categories were derived as discussed. P values were determined using the Mantel-Cox (Log-rank) test and the Hazard Ratio (HR) and Confidence Intervals (CI) were determined by the Mantel-Haenszel test. Serum LDH and S100B data were available from routine testing carried out in the ‘Tubingen’ cohort.

Results

Validation of ‘melanoma-specific’ miRNA expression in an independent cohort of stage III and stage IV FFPE melanoma tumors shows significant differences between stages. The ‘MELmiR-18’ panel (see Materials and Methods for selection criteria) was assessed (along with the endogenous control RNU-6) in a retrospective collection (‘Validation Cohort 1’) of FFPE melanoma tissues derived from stage III melanoma patients (n=82) and stage IV melanoma patients (n=10) (FIG. 1). Each assay had a serial dilution of a positive control sample (known expression for all miRNAs in panel) that had a total input of 1 ng, 3 ng, 15 ng, and 45 ng in the original cDNA reaction. The BioMark HD Real-time PCR system (Fluidigm) was used to maximize the chances for miRNA detection. Detectable expression was observed in all dilutions of the positive control (except miR-4469 which had assay failure) as well as being expressed to varying degrees in the FFPE tissue-derived RNA. Grouping of the samples into stages (III vs IV) allowed for statistically significant separation (p.0.031-p.<0.0001) in 13/18 miRNAs with 4/18 (miR-145-5p, miR-363-3p, miR-4706, and miR-514b) being non-significant (ns). The most strongly significant miRNA was miR-4731 (p.<0.0001) followed by miR-4487 (p.0.001) followed by most of the members of the miR-506-514-cluster (Table 4 and FIG. 5). Subsequent ROC analysis (stage III vs IV) confirmed the Mann-Whitney U tests where all previously identified significant miRNAs also had ‘good’ to ‘strong’ AUC scores ranging from 0.709-0.854. (Table 4). The highest AUC scores were 0.854 (miR-4731) and 0.821 (miR-4487). Members of the miR-506-514 cluster had AUC scores ranging from (0.709-0.793) with the highest scores being shared by miR-508-3p and miR-509-5p.

Circulating miRNA expression is readily detectable in serum using a high-throughput ultra-sensitive method of detection. The same ‘MELmiR-18’ panel was then assessed (along with a spiked-in C. elegans (cel-miR-39) miRNA for normalization purposes) in a large series of retrospective and prospective cohorts of patient sera (‘Validation Cohort 2’) with varied stages of disease: no melanoma (n=102), no melanoma but high mole count (n=12), prior history of melanoma (n=16), and stage IV melanoma patients (n=119) (FIG. 1). An ultra-sensitive, high-throughput method of detection (Biomark HD, Fluidigm in combination with TaqMan® MicroRNA Assays, Life Technologies) was used to maximize the chance of detecting low abundance miRNAs which may have been present in the specimens. In miRNA derived from serum, detectable expression was measured in 13/18 miRNAs (Table 2) with 5/18 miRNAs showing no detectable expression. Those miRNAs that had no expression included: miR-4469, miR-508-3p, miR-508-5p, miR-513b, and miR-513c (data not shown). In the 13/18 miRNAs having detectable expression, eight (miR-16, miR-204-5p, miR-211-5p, miR-4487, miR-4706, miR-4731, miR-509-3p, and miR-509-5p) of these showed highly significant differences (Mann-Whitney U-test: p<0.0001) between ‘controls’ (no melanoma, no melanoma but high mole count, and a prior history of melanoma) and patients with stage IV disease (Tables 2 and 5 shows the p values and AUC scores when compared to healthy controls alone). Importantly, the eight highly significant (p<0.0001) miRNAs represented in FIG. 5 show that miR-16 and miR-211-5p have increased expression in melanoma cases compared to controls and the remainder have lower expression as compared to controls.

Table 2 also highlights the significance of the 13 detectable miRNAs in relation to stage I/II (n=86) vs. ‘controls’ along with stage III (n=50) vs. ‘controls’ (‘Validation Cohort 3’). It is important to note that the same highly significant miRNAs (except miR-211-5p) that discriminated stage IV vs. ‘controls’ are also able to discriminate the lesser stages.

Circulating ‘melanoma-specific’ miRNA expression shows significant differences between stages of melanoma. Highly significant expression (Man-Whitney U-test p<0.0001) differences were also observed between stage III vs. stage IV melanoma patients along with stages I/II vs. stage IV melanoma patients (Table 2 and FIG. 6).

A panel of 7 miRNAs has the ability to diagnose melanoma with high sensitivity and specificity. Receiver operating characteristic (ROC) curve analysis revealed which of the highly significant miRNAs (Table 2) have the potential to be used for diagnostic/prognostic purposes (FIG. 2). Particular attention is paid to those miRNAs that were able to detect all stages of disease (stages I-IV) (Table 2). The following miRNAs fitted these criteria: miR-16, miR-211-5p, miR-4487, miR-4706, miR-4731, miR-509-3p, and miR-509-5p (‘MELmiR-7’). FIG. 7 shows all of the ROC curves associated with stage IV vs. ‘controls’.

Analysis of the ROC curve data (see Materials and Methods) was used to categorise expression levels into ‘high’ or ‘low’ vs. ‘controls’, or ‘normal’ (control-like) (FIG. 2). A diagnostic score was then applied to each sample (see Materials and Methods) which ranged from 0 or 1 (low likelihood of melanoma) and 2-7 (high likelihood of melanoma). Upon applying the derived diagnostic score, the ‘MELmiR-7’ panel was evaluated as a group. We found that it had the ability to diagnose melanoma (independent of stage), when ≧2 miRNAs were expressed (96% sensitivity and ≧80% specificity) (Table 3). The sensitivity of the ‘MELmiR-7’ panel increases to 98% when stage IV is compared to ‘controls’ (FIG. 2). Table 3 provides a summary of the effectiveness of the ‘MELmiR-7’ panel in relation to other stages (see Materials and Methods), particularly for the Diagnostic Odds Ratio (DOR). This ratio was used to determine the lowest diagnostic score possible for the ‘MELmiR-7’ panel yet still maintaining very high sensitivity and specificity.

The ‘MELmiR-7’ panel is more sensitive then serum LDH and S100B in diagnosing stage IV melanoma patients. Stage IV melanoma patients (‘TUBINGEN’ cohort) had serum LDH and S100B levels determined as part of their standard of care regimen. Elevated levels of serum LDH and S100B were found in 39% (27/69) and 63% (43/68) of these patients (data not shown). In the same patients, the ‘MELmiR-7’ achieved 96% (66/69) sensitivity and ≧80% specificity. Specificity could not be determined for serum LDH and S100B as ‘controls’ were not assayed.

Binary logistic regression models predicts the accuracy of the MELmiR-7 panel. In regression analysis, using just the median normalised Ct values, high predictive accuracy was achieved when using a binary logistic regression model (stepwise backwards conditional) which was able to classify stage IV melanoma and ‘controls’ 95% and 94% respectively (FIG. 3). The same accuracy was achieved when in a stepwise fashion; miR-16 and miR-4487 were left out of the model (Table 6). When stage III were compared to ‘controls’, the highest level of accuracy (90% and 99% respectively) was achieved when miR-4731 and miR-509-3p were left out of the model (Table 5). Likewise, when stage I/II were compared to ‘controls’, the same level of predictive accuracy was maintained following 3 stepwise iterations. The highest percentages were 94% and 97% prediction when miR-211-5p and miR-4731 were left out of the model.

Binary logistic regression models predict recurrence in stage III melanoma patients using the MELmiR-7 panel. Interestingly, when stage III patients were compared to stage IV patients using the same regression model, the complete MELmiR-7 panel achieves an accuracy of 80% (40/50) prediction of stage III vs. 92% prediction of stage IV (FIG. 3). Closer inspection of the stage III patients (at time of blood collection) that were misclassified (according to the regression model) as stage IV (10/50) revealed that 60% (6/10) subsequently developed stage IV recurrence. The combinations of stage III vs. stage IV and stage I/II vs. stage IV can be seen in Table 6.

In stage III melanoma patients with known dates of disease recurrence (stage IV diagnosis) following blood-draw (Median follow-up=20 months, Progression free survival=15.7 months), binary logistic regression was able to predict recurrence in 8/13 (62%) patients and no sign of recurrence (NSR) in 33/37 (89%) patients (FIG. 3). The prediction of NSR was increased to 95% (35/37) when only miR-16, miR-4731, and miR-509-5p were measured (see Table 6 for all possible iterations) however prediction for recurrence was reduced to 54% (7/13).

Binary logistic regression models predict short and long survival in stage IV patients using the MELmiR-7 panel. Stage IV melanoma patients can be classified as short (<2years) and long (>2 years) survivors using the ‘MELmiR-7’ panel. Using binary logistic regression models, short and long survivors have a prediction of 85% (57/67) and 64% (33/52) respectively (FIG. 3). The highest level of prediction for short survivors increases to 88% (59/67) when only miR-211-5p, miR-4706, miR-4731, and miR-miR-509-5p are measured (see Table 6 for all possible iterations).

Binary logistic regression models predict M1a-c stage in stage IV melanoma patients using the MELmiR-7 panel. Stage IV patients are classed as M1a, M1b, and M1c, depending upon the site of distal metastasis. In stage IV patients (MIA cohort; n=40) previously classed as M1a-c at last follow-up, using binary logistic regression, the MELmiR-7 panel was able to predict M1a in 14/14 (100%) cases and M1b in 4/6 (67%) cases respectively (FIG. 3). Prediction of M1b was increased to 83% (5/6) and was decreased to 93% (13/14) for M1a, when only miR-16, miR-4487, miR-4706, miR-4731, and miR-509-3p were expressed. When M1b was compared to M1c, the complete MELmiR-7 panel was able to predict 4/6 (67%) and 15/16 (94%) cases respectively. However, when M1a cases were compared to M1c, 100% prediction was achieved respectively (FIG. 3) (see Table 6 for all possible iterations).

miRNA expression is strongly associated with survival in stage IV patients. The individual miRNAs from the ‘MELmiR-7’ panel identified following ROC analysis were then assessed for their prognostic ability in terms of survival using date of last follow-up data along with alive/dead from melanoma status (up to 5 years post stage IV diagnosis). One miRNA, miR-211-5p, showed statistically significant association with survival of stage IV patients. Kaplan-Meier survival curve analysis identified high miR-211 expression as the most strongly significant predictor of survival, with median survival being 1.1 years vs. 3.5 years (HR 3.2, 95% CI 2.02-5.14, p<0.0001) (FIG. 4) for high and normal miR-211-5p expression.

Additionally, for the stage IV patients (Tubingen cohort) that had S100B (n=67) and serum LDH (n=69) status available, median survival times observed for elevated S100B, serum LDH, and miR-211-5p, was 1.21, 1.22, and 1.26 years respectively (data not shown).

Discussion

The linear progression model for melanoma has been in place for many years and by and large, most of the observable tumors fit into this framework. Staging of melanoma adheres to strict guidelines devised by the American Joint Committee on Cancer (AJCC) 1 and is based upon evidence-based gross observation and currently available serological markers (eg serum LDH). This is a highly effective system and dictates the level of treatments which are offered to melanoma patients. However, this model which does not take into account the presence of metastatic disease unobservable to the treating physician by conventional methods (e.g. palpation, CT scans). Currently, there are no serological tests available that are sensitive enough to detect the presence of melanoma in a patient before it manifests into ‘observable’ disease.

We have successfully confirmed the expression of a ‘melanoma-specific’ panel of miRNAs that are not only diagnostically accurate in FFPE tissue, distinguishing stage III from stage IV tumours, but also, via a minimally-invasive blood test, a proportion of these miRNAs (‘MELmiR-7’) allow for highly accurate diagnosis and prognosis of melanoma patients (independent of AJCC stage).

miRNAs are commonly found in the circulation and have been associated with cancer progression^(13, 14). miRNAs are also inherently resistant to degradation (if contained within exosomes or bound to AGO2)13-15 and are readily detectable in serum and plasma, using precise methods of isolation and detection. Using a comprehensive approach to identify a panel of miRNAs (MELmiR-18)²⁰ that could be deemed ‘melanoma-specific’, combined with using an ultra-sensitive method of detection, we have successfully identified a highly sensitive (96%) and specific (≧80%) diagnostic panel of seven miRNAs (‘MELmiR-7’ including: miR-16, miR-211-5p, miR-509-3p, miR-509-5p, mir-4487, miR-4731-5p, and miR-4706) which have the ability to correctly diagnose melanoma (n=242) independent of AJCC stages I-IV. Most importantly, the sensitivity increases to 98% in stage IV patients (n=119) when 2 or more miRNAs are expressed as compared to ‘controls’. In fact, the panel also achieved high sensitivity and specificity in stage I/II (98% and ≧80% respectively) as well as stage III (91% and ≧79% respectively) thus paving the way for early diagnosis to be achieved.

There is currently an unmet need for a highly specific and predictive serum biomarker of melanoma burden, as for many years the consensus whether or not to use currently available serological markers (S100B and serum LDH) is often disputed due to the ranges of sensitivity and specificities that some studies have reported^(4-8, 10-12). As such, dependent upon the local treatment regimen, these are not commonly used. Despite the lack of consensus for sensitivity and specificity, a recent study did find that elevated levels of S100B were able to predict survival times in unresectable melanoma patients²⁴.

We have subsequently found that elevated expression levels of miR-211-5p (a member of our MELmiR-7 panel) have the ability to predict prognosis in stage IV melanoma patients. Interestingly in the same group of patients, these survival data mirror those found for S100B, thus making the MELmir-7 panel not only highly sensitive as a diagnostic panel, but also highly informative in terms of prognosis.

Stage IV patients are further categorized into M-stage which is given according to where the tumor has metastasized (M1a: skin, subcutaneous, or distant lymph nodes; M1b: lung; and M1c: other visceral metastases or any distant metastasis with elevated serum LDH)1. We have found that the MELmir-7 panel allows for 100% discrimination between M1a and M1c. These data are highly suggestive of the level of MELmiR-7 panel expression being relative to the presence of tumour in the circulation. This panel would therefore be ideally suited to monitor tumour progression in patients diagnosed with late stage disease (IV) following treatment to enable a change in therapy following a subsequent recurrence. In addition, in a cohort of stage III melanomas (MIA) with known dates of recurrence, the MELmiR-7 panel was able to predict recurrence in 62% of patients and confirm no sign of recurrence (NSR) in 89% of patients.

According to the AJCC Staging committee, stage III melanoma patients have a 50% chance of survival beyond 5 years 1; these patients also remain the most difficult to provide effective treatments/surveillance regimens and accurate survival estimates. Melanoma patients diagnosed with stage III disease have tumours that have spread to regional lymph nodes or have developed in-transit metastases or satellites but have no evidence of distant metastasis. The treatment for stage III melanoma relies heavily on the accuracy of the clinical staging of the disease and (which can vary across melanoma centres) as the most effective treatment is surgical removal of the affected lymph nodes/satellites25. Following treatment, stage III patients are subjected to a series of standard physical examinations including computer tomography (CT) scans and serum LDH/S100B (depending on treatment centre) testing every 3 months for the first year, every 4 months in the second year, every 6 months in the third-fifth year, and then yearly for every subsequent year after 5 years. The frequency of these tests is deemed necessary for early detection of distant metastases; however this causes a significant burden to both the patient and the healthcare system. To alleviate this burden, the MELmiR-7 panel could be offered to patients to complement physical examination. If the diagnostic score for melanoma positivity has changed from earlier measurements, then this may indicate the presence of disease recurrence and as such, these patients may qualify earlier for adjuvant, systemic, or targeted therapies that would other-wise be only offered to stage IV patients. The use of this miRNA panel in this manner has the potential to greatly increase the chances of survival by earlier and more precise detection of the presence of metastases.

In terms of prognosis, miR-211-5p measurement may also allow better triaging of patients diagnosed with stage IV disease, into good and poor prognosis which would be highly informative for not only the treating clinician but also for the quality of life of the patients.

TABLE 3 Summaries of diagnostic test statistics generated when AJCC staged melanoma is compared with controls. MELmiR-panel MELANOMA Stage I/II Stage III Stage IV Diagnostic ≧2 ≧3 ≧2 ≧3 ≧2 ≧3 ≧2 ≧3 Score Sensitivity 96% 91% 98% 92% 91% 86% 98% 93% Specificity 80% 90% 80% 90% 80% 90% 80% 90% False Positive 20% 10% 20% 10% 20% 10% 20% 10% Rate False Negative  4%  9%  2%  8%  9% 14%  2%  7% Rate Positive 89% 94% 72% 85% 60% 77% 78% 89% Predictive Value (PPV) Negative 93% 84% 98% 95% 96% 94% 98% 94% Predictive Value (PPV) Likelihood 4.75 8.84 4.83 8.94 4.49 8.33 4.82 8.99 Ratio Positive Likelihood 0.05 0.10 0.03 0.08 0.11 0.15 0.03 0.08 Ratio Negative Diagnostic 100.45 92.08 169.39 106.48 39.39 54.17 156.26 114.59 Odds Ratio (DOR)

TABLE 6 Tables were compiled following Binary Logistic Regression analysis performed in SPSS for Windows. version 21.0. To represent the overall analysis performed for each cohort. only the “classification table” and the “variables in the equation” are shown. Controls vs Stage I/II Classification Table^(a) Predicted Cohorts Percentage Observed Controls Stage I/II Correct Step 1 Cohorts Controls 126 4 96.9 Stage I/II 5 81 94.2 Overall Percentage 95.8 Step 2 Cohorts Controls 126 4 96.9 Stage I/II 5 81 94.2 Overall Percentage 95.8 Step 3 Cohorts Controls 126 4 96.9 Stage I/II 5 81 94.2 Overall Percentage 95.8 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1^(a) miR16 −1.270 .444 8.169 1 .004 .281 miR211 025 .110 .052 1 .820 1.025 miR4487 1.164 .305 14.579 1 .000 3.204 miR4706 137 .114 1.453 1 .228 1.147 miR4731 .171 .246 .487 1 .485 1.187 miR509.3p −.629 .358 3.096 1 .079 533 miR509.5p 297 .088 11.266 1 .001 1.346 Constant −19.668 7.505 6.867 1 .009 .000 Step 2^(a) miR16 −1.244 .427 8.506 1 .004 .288 miR4487 1.160 .304 14.543 1 .000 3.188 miR4706 .129 .108 1.436 1 .231 1.138 miR4731 185 .239 .602 1 .438 1.203 miR509.3p −.611 344 3.159 1 .076 .543 miR509.5p .299 .088 11.585 1 .001 1.348 Constant −19.677 7.474 6.931 1 .008 .000 Step 3^(a) miR16 −1.248 .416 8.996 1 .003 .287 miR4487 1.218 .304 16.009 1 .000 3.380 miR4706 .165 .101 2.670 1 .102 1.179 mIR509.3p −.576 .336 2.936 1 .087 .562 miR509.5p .278 .082 11.623 1 .001 1.321 Constant −17.449 6.719 6.745 1 .009 .000 ^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. Controls vs Stage III Classification Table^(a) Predicted Cohorts Percentage Observed Controls Stage III Correct Step 1 Cohorts Controls 127 3 97.7 Stage III 5 45 90.0 Overall Percentage 95.6 Step 2 Cohorts Controls 127 3 97.7 Stage III 5 45 90.0 Overall Percentage 95.6 Step 3 Cohorts Controls 128 2 98.5 Stage III 5 45 90.0 Overall Percentage 96.1 Step 4 Cohorts Controls 129 1 99.2 Stage III 6 44 88.0 Overall Percentage 96.1 Step 5 Cohorts Controls 129 1 99.2 Stage III 6 44 88.0 Overall Percentage 96.1 Step 6 Cohorts Controls 129 2 98.5 Stage III 6 44 88.0 Overall Percentage 95.6 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1^(a) miR16 −1.531 .479 10.210 1 .001 .216 miR211 .139 .096 2.101 1 .147 1.149 miR4487 938 .329 8.146 1 .004 2.555 miR4706 158 .129 1.494 1 222 1.171 miR4731 −.118 .289 167 1 683 889 miR509.3p −.118 .362 107 1 .744 889 miR509.5p 090 .091 .984 1 .321 1.094 Constant −12.453 6.711 3.443 1 .064 .000 Step 2^(a) miR16 −1.620 .404 16.060 1 000 .198 miR211 .135 .094 2.029 1 .154 1.144 miR4487 .935 .328 8.110 1 .004 2.548 miR4706 161 .128 1.590 1 .207 1.175 miR4731 −.109 .287 143 1 705 897 miR509.5p .084 0.68 .905 1 .341 1.087 Constant −13.915 5.181 7.213 1 .007 000 Step 3^(a) miR16 −1.595 .394 15.340 1 .000 .203 miR211 .125 091 1.916 1 166 1.134 miR4487 .860 250 11.804 1 .001 2.362 miR4706 .144 118 1.490 1 .222 1.155 miR509.5p 099 078 1.593 1 .207 1.104 Constant −14.800 4.740 9.748 1 .002 .000 Step 4^(a) miR16 −1.537 379 15.455 1 000 .215 miR211 .086 086 1.011 1 315 1.090 miR4487 1.029 239 18.610 1 000 2.799 miR509.5p 103 077 1.815 1 178 1.109 Constant −13.936 4.632 9.052 1 .003 .000 Step 5^(a) miR16 −1.431 343 17.446 1 000 .239 miR4487 1.056 241 19.245 1 .000 2.876 miR509.5p .097 076 1.024 1 203 1.102 Constant −13.081 4.559 8.234 1 .004 000 Step 6^(a) miR16 −1.497 .332 20.273 1 .000 224 miR4487 1.150 226 27.243 1 000 3.255 Constant −12.700 4.451 8.142 1 004 000 ^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. Controls vs Stage IV Classification Table^(a) Predicted Cohorts Percentage Observed Controls Stage IV Correct Step 1 Cohorts Controls 122 8 93.8 Stage IV 6 113 95.0 Overall Percentage 94.4 Step 2 Cohorts Controls 122 8 93.8 Stage IV 7 112 94.1 Overall Percentage 94.0 Step 3 Cohorts Controls 122 8 93.8 Stage IV 6 113 95.0 Overall Percentage 94.4 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig Exp(B) Step 1^(a) miR509.5p 386 090 19.622 1 000 1.474 mir16 209 319 973 1 350 1.347 miR211 −491 101 23.546 1 000 612 miR4487 183 279 434 1 510 1.201 miR4706 233 099 5.488 1 016 1.262 miR4731 938 245 14.669 1 000 2.555 miR509.3p 321 103 9.737 1 002 1.379 Constant −47.261 10.728 19.409 1 .000 000 Step 2^(a) miR509.5p 402 088 20.744 1 .000 1.49 miR16 .280 .306 .935 1 .361 1.323 miR211 −494 100 24.343 1 .000 610 miR4706 238 099 5.728 1 017 1.268 miR4731 1.018 217 22.041 1 000 2.768 miR509.3p 308 094 10.761 1 .001 1.361 Constant −44.729 9.547 21.951 1 .000 .000 Step 3^(a) miR509.5p 399 088 20.659 1 000 1.499 miR211 −484 .098 24.197 1 .000 .616 miR4706 237 .097 5.930 1 .014 1.207 miR4731 1.002 218 21.113 1 .000 2.724 miR509.3p 297 093 10.262 1 001 1.345 Constant −40.322 7.744 27.109 1 000 000 ^(a)Variable(s) entered on step 1: miR509.5p, miR16, miR211, miR4487, miR4706, miR509.3p. Stage I/II vs Stage III Classification Table^(a) Predicted Cohorts Percentage Observed Stage I/II Stage III Correct Cohorts Stage I/II 73 13 84.9 Step 1 Stage III 32 18 36.0 Overall Percentage 66.9 Cohorts Stage I/II 73 13 84.9 Step 2 Stage III 32 18 36.0 Overall Percentage 66.9 Cohorts Stage I/II 76 10 88.4 Step 3 Stage III 35 15 30.0 Overall Percentage 66.9 Cohorts Stage I/II 75 11 87.2 Step 4 Stage III 33 17 34.0 Overall Percentage 67.6 Cohorts Stage I/II 76 10 68.4 Step 5 Stage III 32 18 36.0 Overall Percentage 69.1 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1^(a) miR16 −.157 .159 .982 1 .322 .854 miR211 −.004 048 .005 1 .937 996 miR4487 .305 .097 10.001 1 .002 1.357 miR4706 −.003 .065 .931 1 .335 .939 miR4731 −185 .077 5.886 1 .015 829 miR509.3p .176 .200 .770 1 .380 1.192 miR509.5p −.083 .040 3.193 1 .074 .920 Constant −.532 4.284 .015 1 .991 .587 Step 2^(a) miR16 −.160 158 1.057 1 304 652 miR4487 .306 .098 10.266 1 .001 1.359 miR4706 −0.62 .005 .926 1 336 939 miR4731 −188 .077 5.934 1 .015 828 miR509.3p .173 .197 .772 1 .380 1.189 miR509.9p −.083 .046 3.255 1 .071 .920 Constant −.572 4.253 .018 1 893 564 Step 3^(a) miR16 −.124 .150 .083 1 408 883 miR4487 .318 .095 11.225 1 .001 1.375 miR4706 −054 .064 .709 1 .400 948 miR4731 −195 .077 6.422 1 .011 823 miR509.5p −.080 .046 3.066 1 .080 923 Constant 2.191 2.892 .574 1 .449 8.944 Step 4^(a) miR4487 .295 .058 11.120 1 .001 1.342 miR4706 −.043 .052 .479 1 .489 955 miR4731 −.193 .077 6.354 1 .012 .825 miR509.5p −.080 .048 3.055 1 .060 .923 Constant 953 2.467 .149 1 .099 2.594 Step 5^(a) miR4487 .284 .086 10.781 1 .001 1.328 miR4731 −.212 .072 8.549 1 .003 809 miR509.5p −.087 .044 3.816 1 .051 .917 Constant .430 2.370 .033 1 .856 1.537 ^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. Stage I/II vs Stage IV Classification Table^(a) Predicted Cohorts Percentage Observed Stage I/II Stage IV Correct Step 1 Cohorts Stage I/II 81 5 94.2 Stage IV 10 109 91.6 Overall Percentage 92.7 Cohorts Stage I/II 82 4 95.3 Step 2 Stage IV 10 109 91.6 Overall Percentage 93.2 Cohorts Stage I/II 79 7 91.9 Step 3 Stage IV 11 108 90.8 Overall Percentage 91.2 Cohorts Stage I/II 79 9 90.7 Step 4 Stage IV 12 107 89.9 Overall Percentage 90.2 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig Exp(B) Step 1^(a) miR16 .979 .217 20.407 1 .000 2.661 miR211 −.350 .076 21.463 1 .000 .705 miR4487 −479 .156 9.156 1 .002 823 miR4706 −.065 .058 .570 1 .450 .937 miR4731 170 .101 2.851 1 .091 1.185 miR509.3p .905 217 17.470 1 .000 2.473 miR509.5p −035 065 291 1 .589 955 Constant −11.035 5.856 3.552 1 .059 .000 Step 2^(a) miR16 .964 213 20.579 1 .000 2.623 miR211 −.361 073 24.355 1 .000 697 miR4487 −480 157 9.378 1 .002 619 miR4706 −083 080 1.095 1 .295 920 miR4731 163 099 2.718 1 .099 1.177 miR509.3p .900 .216 17.293 1 .000 2.450 Constant −10.566 5.773 3.350 1 .007 000 Step 3^(a) miR16 954 210 20.857 1 .000 2.596 miR211 −.373 073 25.848 1 .000 .089 miR4487 −506 155 10.657 1 001 003 miR4731 .147 096 2.347 1 .126 1.159 miR509.3p .888 214 17.196 1 .000 2.430 Constant −11.773 5.668 4.314 1 038 000 Step 4^(a) miR16 882 .202 19.127 1 .000 2.417 miR211 −.373 .071 27.606 1 .000 .689 miR4487 −.425 145 8.765 1 003 652 miR509.3p .914 210 18.979 1 .000 2.496 Constant −9.277 5.345 3.012 1 .083 000 ^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. Stage III vs Stage IV Classification Table^(a) Predicted Cohorts Percentage Observed MIA III Stage IV Correct Step 1 Cohorts Stage III 40 10 90.0 Stage IV 9 110 92.4 Overall Percentage 98.8 Step 2 Cohorts Stage III 40 10 80.0 Stage IV 8 111 93.3 Overall Percentage 89.3 Step 3 Cohorts Stage III 40 10 90.0 Stage IV 8 111 93.3 Overall Percentage 89.3 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig Exp(B) Step 1^(a) miR16 1.007 269 14.032 1 .000 2.737 miR211 −300 076 15.503 1 000 740 miR4487 −732 194 14.289 1 000 491 miR4706 020 112 032 1 .859 1.020 miR4731 387 149 6.753 1 .000 1.473 miR509.3p .768 247 9.094 1 .002 2.155 miR509.5p 021 077 071 1 790 1.021 Constant −13.209 6.285 4.417 1 .086 000 Step 2^(a) miR16 1.005 268 14.089 1 .000 2.733 miR211 −299 075 15.840 1 000 742 miR4487 −728 192 14.359 1 000 .493 miR4731 391 148 6.057 1 008 1.478 miR509.3p 764 245 9.751 1 .002 2.147 miR509.5p 029 066 .175 1 675 1.023 Constant −12.880 5.997 4.613 1 032 000 Step 3^(a) miR16 1.013 259 14.170 1 000 2.753 miR211 −.294 073 15.983 1 .000 .746 miR4487 −710 185 14.656 1 000 .492 miR4731 386 147 6.905 1 .009 1.472 miR509.3p 781 243 10.331 1 .001 2.184 Constant −12.954 6.017 4.634 1 031 000 ^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. Stage III (NSR) vs Stage III (REC) Classification Table^(a) Predicted Cohorts Percentage Observeo NSR REC Correct Step 1 Cohorts NSR 33 4 89.2 REC 5 8 81.5 Overall Percentage 82.0 Step 2 Cohorts NSR 32 5 86.5 REC 5 8 81.5 Overall Percentage 80.0 Step 3 Cohorts NSR 34 3 91.9 REC 5 8 61.5 Overall Percentage 84.0 Step 4 Cohorts NSR 33 4 89.2 REC 5 7 53.8 Overall Percentage 80.0 Step 5 Cohorts NSR 35 2 94.8 REC 6 7 53.8 Overall Percentage 84.0 Variables in the Equation B S.E. Wald df Sig Exp(B) Step 1^(a) miR16 941 480 3.842 1 .050 2.563 miR211 −161 .099 2.635 1 .105 852 miR4487 −188 103 1.327 1 .249 .829 miR4706 .027 155 .031 1 .861 1.027 miR4731 −410 .238 2.976 1 .084 .664 miR509.3p .266 .436 .371 1 .542 1.304 miR509.5p .293 121 5.872 1 .015 1.340 Constant −7.428 9.701 .579 1 .447 .001 Step 2^(a) miR16 .903 .423 4.547 1 .033 2.467 miR211 .161 .099 2.650 1 .104 851 miR4487 −.156 .163 1.294 1 .255 .830 miR4731 −389 .209 3.654 1 .055 678 miR509.3p 283 .428 438 1 .508 1.327 miR509.5p .209 .118 6.370 1 .012 1.247 Constant −7.190 9.731 .546 1 .460 .001 Step 3^(a) miR16 .898 .418 4.661 1 .031 2.454 miR211 −.152 .097 2.448 1 .118 .859 miR4487 −.109 .159 1.107 1 .293 .845 miR4731 −.354 .190 3.479 1 .062 .702 miR509.5p .297 .115 6.524 1 .011 1.346 Constant −2.508 6.746 .156 1 .693 .069 Step 4^(a) miR16 .880 .417 4.457 1 .035 2.410 miR211 −.131 .094 1.933 1 .164 .877 miR4731 −.415 .186 4.955 1 .026 .001 miR509.5p 250 .105 5.533 1 .019 1.285 Constant −1.510 6.620 .485 1 .486 .010 Step 5^(a) miR16 .795 .390 4.143 1 .042 2.214 miR4731 −.409 .182 5.067 1 .024 004 miR509.5p .272 .104 6.797 1 .009 1.213 Constant −8.295 5.915 1.982 1 .161 .000 ^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. Surival of Stage IV (<2 yr) vs Stage IV (>2 yr) Classification Table^(a) Predicted Cohorts Short Long Percentage Observed Survivor Survivor Correct Step 1 Cohorts Short Survivor 57 10 85.1 Long Survivor 19 33 63.5 Overall Percentage 75.6 Step 2 Cohorts Short Survivor 56 11 83.6 Long Survivor 19 33 53.5 Overall Percentage 74.8 Step 3 Cohorts Short Survivor 55 12 82.1 Long Survivor 19 33 63.5 Overall Percentage 73.9 Step 4 Cohorts Short Survivor 59 8 88.1 Long Survivor 19 33 63.5 Overall Percentage 77.3 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1^(a) miR16 −101 .191 .279 1 .597 .904 miR211 .230 .056 16.789 1 .000 1.259 miR4487 071 134 .282 1 .595 1.074 miR4706 .255 .088 8.439 1 004 1.290 miR4731 .097 .083 1.356 1 244 1.102 miR509.3p −008 039 048 1 826 .992 miR509.5p −113 051 4.978 1 026 .893 Constant −15.131 7.002 4.590 1 032 .000 Step 2^(a) miR16 −.089 183 .239 1 625 915 miR211 228 .053 18.199 1 000 1.254 miR4487 .071 134 .285 1 593 1.074 miR4706 258 088 8.541 1 003 1.292 miR4731 .094 .082 1.303 1 .254 1.099 miR509.5p −115 .050 5.163 1 023 892 Constant −15.338 6.997 4.904 1 028 .000 Step 3^(a) miR211 .228 053 18.065 1 000 1.254 miR4487 098 123 634 1 426 1.103 miR4706 257 088 5.601 1 003 1.293 miR4731 115 .071 2.610 1 108 1.122 miR509.5p −.112 .050 5.015 1 025 .894 Constant −17.804 4.762 14.122 1 000 .000 Step 4^(a) miR211 219 052 17.917 1 000 1.244 miR4706 .257 087 8.830 1 003 1.293 miR4731 142 .063 5.026 1 .025 1.152 miR509.5p −109 .050 4.859 1 .028 .897 Constant −15.991 4.029 15.756 1 000 000 ^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. M Stage Prediction Classification Table^(a) Predicted Cohorts Percentage Observed M1a M1c Correct Step 1 Cohorts M1a 14 0 100.0 M1c 0 16 100.0 Overall Percentage 100.0 ^(a)The cut value is .500 Variables in the Equation B S.E wald df Sig Exp(B) Step 1^(a) miR16 −88.890 3906.283 .001 1 .980 000 miR211 11.432 435.925 001 1 979 92223.759 miR4487 195.085 6137.855 001 1 975 5.299E+84 miR4706 −157.880 5104.067 .001 1 975 000 miR4731 −97.471 3095.981 001 1 975 000 miR509.3p −37.669 1191.835 001 1 975 .000 miR509.5p −17.797 614.043 .001 1 977 000 Constant 6098.464 1.971E+05 .001 1 975 ^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. Classification Table^(a) Predicted Cohorts Percentage Observed M1a M1b Correct Step 1 Cohorts M1a 14 0 100.0 M1b 2 4 66.7 Overall Percentage 90.0 Step 2 Cohorts M1a 14 0 100.0 M1b 2 4 66.7 Overall Percentage 90.0 Step 3 Cohorts M1a 13 1 92.9 M1b 1 5 53.3 Overall Percentage 90.0 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig Exp(B) Step 1^(a) miR16 −.721 1.078 .448 1 .503 486 miR211 145 220 435 1 510 1.156 miR4487 1.157 .854 1.835 1 176 3.182 miR4706 −.790 .527 2.249 1 .134 .454 miR4731 −.490 .354 1.918 1 .166 .613 miR509.3p −270 .215 1.576 1 .209 .764 miR509.5p −.010 .213 .002 1 .962 .690 Constant 25.245 20.936 .711 1 .399

Step 2^(a) miR16 −.693 .888 .609 1 .435 .500 miR211 .145 .219 .435 1 .508 1.150 miR4487 1.165 .847 1.892 1 .169 3.205 miR4706 −.801 .481 2.776 1 .096 .449 miR4731 −.487 .350 1.941 1 .164 .614 miR509.3p −.267 .205 1.697 1 .193 .766 Constant 24.617 25.819 .843 1 .359

Step 3^(a) miR16 −.935 .878 1.133 1 .287 .393 miR4487 .910 .584 1.769 1 .183 2.485 miR4706 −.728 .425 2.909 1 .058 .484 miR4731 −.507 .344 2.174 1 .140 .602 miR509.3p −221 .155 2.048 1 .152 .801 Constant 34.560 25.078 1.899 1 .108

^(a)Variable(s) entered on step 1: miR16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p. Classification Table^(a) Predicted Cohorts Percentage Observed M1b M1c Correct Step 1 Cohorts M1b 4 2 66.7 M1c 1 15 93.8 Overall Percentage 86.4 Step 2 Cohorts M1b 4 2 66.7 M1c 1 15 93.8 Overall Percentage 86.4 Step 3 Cohorts M1b 4 2 66.7 M1c 1 15 93.8 Overall Percentage 86.4 Step 4 Cohorts M1b 2 4 33.3 M1c 1 15 93.8 Overall Percentage 77.3 Step 5 Cohorts M1b 2 4 33.3 M1c 2 14 87.5 Overall Percentage 72.7 Step 6 Cohorts M1b 2 4 33.3 M1c 1 15 93.8 Overall Percentage 77.3 Step 7 Cohorts M1b 1 5 16.7 M1c 0 16 100.0 Overall Percentage 77.3 Step 8 Cohorts M1b 0 6 0.0 M1c 0 16 100.0 Overall Percentage 72.7 ^(a)The cut value is .500 Variables in the Equation B S.E. Wald df Sig Exp(B) Step 1^(a) miR16 1.034 1.230 697 1 .404 2.813 miR211 −.191 .133 2.055 1 .152 .826 miR4487 .385 .419 .854 1 .355 1.473 miR4706 .039 .220 .031 1 .851 1.030 miR4731 −.377 .291 1.075 1 .196 .686 miR509.3p −103 .122 .707 1 .401 .903 miR509.5p .008 .140 .003 1 .053 1.008 Constant −2.212 16.670 .015 1 .894 .109 Step 2^(a) miR16 1.014 1.189 .727 1 .394 2.757 miR211 −188 .121 2.393 1 .122 .829 miR4487 .390 .419 .865 1 .352 1.477 miR4706 .043 .208 .044 1 .834 1.044 miR4731 −376 .292 1.059 1 .198 .086 miR509.3p −102 .122 .702 1 .402 .905 Constant −2.079 10.453 .015 1 .899 .125 Step 3^(a) miR16 .911 1.062 .736 1 .391 2.487 miR211 −.181 .115 2.458 1 .117 .835 miR4487 .415 .402 1.064 1 .302 1.514 miR4731 −.392 .281 1.948 1 .163 .076 miR509.3p −.098 .120 .869 1 .413 .907 Constant .037 13.133 .000 1 .998 1.038 Step 4^(a) miR16 .002 609 .553 1 .457 1.825 miR211 −.202 115 3.055 1 .080 .817 miR4487 .349 .360 .943 1 .332 1.416 miR4731 −.318 .232 1.883 1 .170 .727 Constant .193 12.401 .000 1 988 1.213 Step 5^(a) miR211 −160 .094 2.905 1 .066 .852 miR4487 .255 .332 .591 1 .442 1.290 miR4731 −.314 .215 2.127 1 145 .731 Constant 8.004 8.765 .826 1 .361 2891.756 Step 6^(a) miR211 −.149 .091 2.707 1 .100 .861 miR4731 −.233 .186 1.532 1 .216 .782 Constant 12.449 7.167 3.140 1 .082

Step 7^(a) miR211 −128 .089 2.087 1 148 .880 Constant 4.287 2.418 3.140 1 .076 72.747 Step 8^(a) Constant .981 .479 4.198 1 .040 2.667 ^(a)Variable(s) entered on step 1: mir16, miR211, miR4487, miR4706, miR4731, miR509.3p, miR509.5p

EXAMPLE 2 Materials and Methods

Cell Culture and Total RNA extraction. All melanoma (cutaneous and uveal) cell lines (Table 8) were established and have been previously described63, 64. All other solid cancer cell lines (Table 8) were kind donations from investigators at QIMR Berghofer. Most of the solid cancer cell lines are available from the cell line repositories ATCC or CellBank Australia.

All cell lines were cultured in RPMI (#31800-089, Life Technologies, Foster City, Calif., USA) supplemented with 10% FBS (Life Technologies, Foster City, Calif., USA) with HEPES, 100 U/ml penicillin and 100 μg/ml streptomycin (Life Technologies, Foster City, Calif., USA) at 37° C. (5% CO2) and were periodically authenticated via short tandem repeat profiling according to the manufacturer's instructions (AmpFISTR Profiler Plus ID kit; Life Technologies. Foster City, Calif., USA). Primary human melanoblasts (QF1160MB) and melanocytes (MELA) were established from human neonatal foreskin and cultured as described^(65, 66). Cells were harvested from the plate and column-purified using the miRNeasy Kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions.

Serum/Plasma collection and Total RNA extraction. Ethical approval was granted by the QIMR Berghofer's Human Research Ethics Committee (HREC) approval number P1237. Serum and Plasma were processed using standard methodologies and total RNA was extracted using the Plasma/Serum Circulating RNA Purification Kit (#30000; Norgen Biotek, Ontario, Canada) according to manufacturer's instructions.

microRNA microarray profiling and data analysis. 5 μg of total RNA was shipped to LC Sciences (Houston, USA) for miRNA profiling using a custom array platform (μParaflo® technology) containing 1898 miRNAs (miRBase V18)(67). All QC, labelling (Cy-3), hybridization, scanning, signal background subtraction and global normalisation (LOWES) were performed by LC Sciences as per technical note: (www.lcsciences.com/documents/application-notes/Tech-Bull-MicroRNA-Microarmy-Data-Analysis.pdf).

Advanced data analyses were performed in Genespring GX12.5 (Agilent Technologies, Santa Clara, USA) using the LOWES normalised signal intensity values. All values <30 were considered ‘background expression’ (personal communication with LC Sciences) and changed to 0.01 prior to log₂ transformation. So as to identify ‘melanoma-specific’ miRNAs that were potentially more relevant to melanoma, samples were classified as either ‘melanoma’ or ‘other ca’ (melanocytes, melanoblasts. nevocyte, and serum derived samples were excluded from these categories). To identify differentially expressed miRNAs, a Mann-Whitney U-test (unpaired) was applied to a ‘volcano-plot’ analysis with thresholds set at p<0.05 and ≧2 fold. The gene list derived from these analyses was then applied to all samples in an unsupervised manner using hierarchical clustering (Euclidean similarity with average linkage).

miScript quantitative RT-PCR validation. A selection of miRNAs was validated using quantitative real-time-PCR to check the integrity of the microarray data. Briefly, all samples included on the microarray along with an extended cohort of melanoma cell lines (as described in ref 54) were reverse transcribed using the miScript II RT Kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. Real-time PCR was subsequently performed with a miScript SYBR Green PCR Kit (QIAGEN, Hilden, Germany) using the 7900HT Fast Real Time PCR System (Life Technologies, Foster City, Calif., USA). Data were analysed in Microsoft Excel using the ΔCT method compared to RNU6 which was assessed in every sample.

Results

‘Melanoma-specific’ miRNA discovery profiling in melanoma. This comprehensive analysis of a large panel of melanoma cell lines identified distinct ‘tissue-specific’ expression as compared to other solid malignancies. We used miRNA microarrays (LC Sciences, Houston, USA. miRBaseV18; 1898 miRNAs) to comprehensively study the miRNA profile of cutaneous melanoma (n=55), uveal melanoma (n=7), and RNA derived from melanoma patient scrum/plasma (n=3) in relation to ‘other’ solid malignancies (n=34). Normal pigment (melanocytes and melanoblasts) and pre-malignant (nevocyte) cells were also included as controls (‘Discovery cohort’). In this ‘Discovery’ set, a total of 233/1898 differentially expressed (≧2 fold, p<0.05; See Materials and Methods) miRNAs were identified which may fit this criteria when ‘melanomas’ were compared to ‘other’ cancers (FIG. 8 and Table 7). We identified a significant number of miRNAs (Table 8) with known and novel relationship to melanoma. Table 9 summarizes the top up and down regulated miRNAs by at least 10 fold, most of which have a known association either with melanoma or tumourigenesis.

‘Lineage-specific’ miRNA validation. Notably the top two miRNAs up-regulated in melanoma were hsa-miR-211-5p (miR-211) and hsa-miR-514a-3p (miR-514a) with profound average fold changes of 276 and 204 respectively compared to other solid malignancies (Table 8). To test the robustness of the array data, quantitative real-time PCR validation was performed with these miRNAs along with a selection of other miRNAs from the array (FIG. 9 and Table 10).

Discussion

MicroRNAs have been proven to be powerful regulators of protein coding gene expression in almost all facets of cell biology. In cancer, their dysregulation can be far reaching, spanning a multitude of different signalling pathways due to the number of genes being directly and indirectly regulated and as such, they have been intensively studied for over a decade. In melanoma, the comprehensive analysis of miRNA has been limited, with most studies performed using only a few hundred miRNAs²⁸. Newly characterised miRNAs have seen the expansion of miRBASE (curated miRNA database), largely due to the advent of deep sequencing technologies. As most of the newly identified miRNAs have no known association with melanoma, here we sort to survey a large panel of melanoma cell lines in relation to other solid malignancies to strengthen prior studies into melanoma-specific miRNAs and to identify those that were previously unknown to be involved in melanomagenesis. Identification of a panel in a comprehensive manner such as this has the ability to open new avenues for research in this burgeoning field, which may provide a greater understanding of this complex disease. In our comprehensive analysis of 1898 miRNAs (miRBaseV18), we have successfully identified a large number of miRNAs (n=233) that were found to be statistically significant (P<0.05, ≧2 fold) when melanoma (n=55) was compared with other solid cancer (n=34). Indeed, many of the identified miRNAs have a known association with melanoma however most have no known association. The main premise of the study was to identify a panel of melanoma-specific miRNAs (or more predominantly expressed) and these are more readily observed in the up-regulated collection of miRNAs. However it is worth noting that many of the down-regulated miRNAs have been previously associated with melanoma. Our dataset confirms and strengthens the associated loss of the miR-200 family (including miR-141)^(47, 48) along with the frequently silenced miRNA, miR-205⁴⁹⁻⁵³.

TABLE 7 Differentially expressed miRNAs in melanoma GeneName p (Corr) p FC (abs) Regulation hsa-miR-200c-3p 1.29E−06 2.24E−08 250.7467 down hsa-miR-204-5p 5.50E−06 1.39E−07 93.67072 up hsa-miR-145-5p 1.31E−04 6.27E−06 54.31673 up hsa-miR-211-5p 3.43E−07 3.97E−09 276.8409 up hsa-miR-363-3p 0.001081 8.32E−05 17.00228 up hsa-miR-510 0.001869 1.69E−04 11.33646 up hsa-miR-514a-5p 0.004085 4.44E−04 8.814804 up hsa-miR-509-3-5p 3.46E−06 7.48E−08 55.9232 up hsa-miR-509-3p 5.15E−07 6.24E−09 139.3508 up hsa-miR-514a-3p 2.96E−07 3.12E−09 204.0894 up hsa-miR-506-3p 1.47E−04 7.38E−06 27.69136 up hsa-miR-509-5p 2.29E−06 4.59E−08 65.2407 up hsa-miR-513c-5p 1.56E−04 7.97E−06 26.93033 up hsa-miR-508-3p 7.57E−05 3.03E−06 31.90864 up hsa-miR-513b 1.08E−05 3.13E−07 61.25974 up hsa-miR-205-5p 0.001084 8.52E−05 19.47374 down hsa-miR-200a-5p 4.47E−06 1.06E−07 31.4116 down hsa-miR-200c-5p 2.14E−04 1.17E−05 13.61658 down hsa-miR-141-3p 1.08E−08 5.70E−11 206.6276 down hsa-miR-203 2.10E−08 1.44E−10 273.6629 down hsa-miR-200b-3p 3.83E−10 1.01E−12 1717.021 down hsa-miR-200a-3p 6.01E−10 1.90E−12 680.575 down hsa-miR-429 3.83E−10 9.84E−13 515.7665 down hsa-miR-183-5p 3.29E−11 1.73E−14 220.7499 down hsa-miR-96-5p 1.73E−08 1.10E−10 120.4834 down hsa-miR-299-3p 0.001145 9.29E−05 10.29763 down hsa-miR-183-3p 4.11E−09 1.73E−11 101.0339 down hsa-miR-375 1.84E−05 6.29E−07 34.56165 down hsa-miR-335-3p 9.59E−07 1.36E−08 40.52689 down hsa-miR-335-5p 2.00E−07 1.89E−09 138.4271 down hsa-miR-27b-5p 0.002927 2.98E−04 9.261446 down hsa-miR-4520a-3p 0.022563 0.003721 6.89 down hsa-miR-224-5p 1.05E−06 1.66E−08 93.71726 down hsa-miR-452-5p 3.02E−06 6.36E−08 28.70594 down hsa-miR-2115-3p 1.64E−04 8.45E−06 13.62161 down hsa-miR-610 0.006736 8.06E−04 6.325314 down hsa-miR-4474-5p 0.004085 4.46E−04 7.169808 down hsa-miR-4768-3p 3.71E−04 2.23E−05 11.11902 down hsa-miR-3120-3p 9.80E−07 1.50E−08 25.58355 down hsa-miR-3131 0.002805 2.81E−04 8.680573 down hsa-miR-3064-5p 0.03839 0.006966 4.001939 down hsa-miR-3146 0.001636 1.43E−04 9.022174 down hsa-miR-29b-2-5p 4.74E−06 1.17E−07 24.81251 down hsa-miR-23b-5p 0.010427 0.001494 5.084345 down hsa-miR-29c-5p 0.018817 0.002954 4.759145 down hsa-miR-181c-3p 0.003249 3.41E−04 6.808747 down hsa-miR-934 0.004177 4.58E−04 5.713632 down hsa-miR-224-3p 8.00E−04 5.69E−05 6.372761 down hsa-miR-3664-3p 0.046873 0.008718 2.658377 down hsa-miR-182-3p 0.004453 4.97E−04 3.530612 down hsa-miR-26b-3p 0.007891 9.94E−04 3.759939 down hsa-miR-1224-3p 0.02713 0.004603 3.188798 down hsa-miR-4639-3p 0.046873 0.008718 2.577534 down hsa-miR-136-3p 0.009735 0.001349 3.622702 down hsa-miR-889 0.022175 0.003622 2.626288 down hsa-miR-146a-3p 0.006231 7.35E−04 8.144401 up hsa-miR-551b-3p 0.002793 2.75E−04 13.10246 up hsa-miR-4436b-5p 0.029436 0.005087 5.61163 up hsa-miR-4655-3p 0.007966 0.001032 9.83113 up hsa-miR-3909 0.001779 1.60E−04 9.591683 up hsa-miR-4469 0.026951 0.004558 4.714384 up hsa-miR-199b-5p 0.010553 0.001518 6.022791 up hsa-miR-20b-3p 0.030173 0.005262 4.286351 up hsa-miR-3189-5p 0.010122 0.001419 6.377651 up hsa-miR-3944-3p 0.010203 0.001441 6.720376 up hsa-miR-3972 0.002615 2.54E−04 7.432085 up hsa-miR-4654 0.047201 0.008845 2.915873 up hsa-miR-3677-5p 0.046873 0.008704 3.970655 up hsa-miR-4785 0.033194 0.005929 3.763893 up hsa-miR-4665-5p 0.047031 0.008772 4.908286 up hsa-miR-646 0.028359 0.004856 3.96194 up hsa-miR-4722-3p 0.006789 8.18E−04 7.179308 up hsa-miR-4731-5p 1.47E−04 7.29E−06 24.1787 up hsa-miR-31-3p 0.001084 8.62E−05 22.97718 down hsa-miR-31-5p 0.003738 4.00E−04 7.086216 down hsa-miR-5701 0.008838 0.001197 10.01555 up hsa-miR-4781-5p 0.014003 0.002132 5.305426 up hsa-miR-548q 0.004951 5.61E−04 5.547026 up hsa-miR-4708-3p 0.001086 8.76E−05 12.24264 up hsa-miR-654-5p 0.009938 0.001386 7.699157 up hsa-miR-658 0.010971 0.001607 6.930552 up hsa-miR-4719 0.007075 8.69E−04 11.2437 down hsa-miR-4539 0.021122 0.003405 6.615453 up hsa-miR-4321 0.008674 0.00117 8.399978 up hsa-miR-92a-1-5p 0.032566 0.005782 5.652854 down hsa-let-7a-3p 0.031593 0.005576 4.507036 down hsa-miR-16-1-3p 0.010709 0.001557 7.805436 down hsa-miR-3127-5p 0.021434 0.003479 4.025753 down hsa-miR-149-5p 1.47E−04 7.24E−06 7.747861 down hsa-miR-103b 0.013613 0.002044 5.532397 down hsa-miR-1306-3p 4.41E−04 2.74E−05 5.499783 down hsa-miR-4277 0.019562 0.003082 4.243334 down hsa-miR-7-1-3p 0.013779 0.002091 7.00258 down hsa-miR-192-5p 0.008451 0.001122 5.301906 down hsa-miR-194-5p 0.010414 0.001487 6.899473 down hsa-miR-625-3p 6.00E−04 3.98E−05 5.362473 down hsa-miR-1244 1.30E−05 4.04E−07 13.87607 down hsa-miR-3609 0.00615 7.19E−04 5.480611 down hsa-miR-374a-5p 0.005488 6.33E−04 5.316285 down hsa-miR-374b-5p 7.74E−04 5.45E−05 4.971394 down hsa-miR-374c-5p 0.002805 2.80E−04 6.082301 down hsa-miR-4644 7.74E−04 5.47E−05 5.508903 down hsa-miR-3148 2.36E−04 1.33E−05 3.753175 down hsa-miR-670 1.31E−04 6.20E−06 2.954435 down hsa-miR-3125 1.56E−05 5.17E−07 10.45982 down hsa-miR-3682-3p 8.17E−05 3.31E−06 13.37263 down hsa-miR-617 1.49E−05 4.87E−07 10.12681 down hsa-miR-196b-3p 0.035312 0.006363 2.420995 down hsa-miR-631 0.047798 0.008991 2.766758 down hsa-miR-4712-3p 0.008318 0.001099 4.874474 down hsa-miR-1183 4.14E−06 9.60E−08 13.50259 down hsa-miR-1305 1.48E−05 4.76E−07 8.817349 down hsa-miR-3616-3p 0.01974 0.00312 3.940568 down hsa-miR-4750 0.007443 9.29E−04 4.045383 down hsa-miR-622 5.67E−06 1.46E−07 6.138279 down hsa-miR-1914-5p 0.0043 4.75E−04 4.031873 up hsa-miR-557 0.002559 2.45E−04 3.978653 up hsa-miR-3937 7.27E−05 2.87E−06 8.556762 up hsa-miR-612 0.018014 0.002819 2.968695 up hsa-miR-498 0.01123 0.001651 5.406655 up hsa-miR-596 3.88E−04 2.39E−05 4.02502 up hsa-miR-637 5.32E−04 3.42E−05 3.097873 up hsa-miR-1914-3p 5.84E−04 3.84E−05 3.166935 up hsa-miR-4783-5p 6.75E−04 4.59E−05 2.468174 up hsa-miR-593-5p 4.55E−05 1.70E−06 7.19713 up hsa-miR-138-5p 0.001269 1.06E−04 7.184394 up hsa-miR-146a-5p 3.50E−10 5.54E−13 36.32244 up hsa-miR-4468 0.007443 9.29E−04 3.498524 down hsa-miR-4435 0.001689 1.49E−04 3.713703 down hsa-miR-4675 0.011764 0.001742 3.270106 down hsa-miR-4303 7.50E−04 5.18E−05 4.217161 down hsa-miR-200b-5p 9.31E−04 6.99E−05 4.466938 down hsa-miR-505-3p 0.030783 0.005417 2.509697 down hsa-miR-4689 0.002805 2.80E−04 2.843713 down hsa-miR-182-5p 1.59E−10 1.67E−13 16.29153 down hsa-miR-3126-3p 0.005012 5.70E−04 2.052046 down hsa-miR-134 0.010971 0.001605 2.169331 down hsa-miR-135b-3p 0.021434 0.00349 2.782404 down hsa-miR-15b-3p 3.97E−05 1.40E−06 4.796393 down hsa-miR-16-2-3p 0.005456 6.27E−04 3.170327 down hsa-miR-7-5p 5.58E−08 4.12E−10 8.046594 down hsa-miR-98 9.47E−04 7.18E−05 2.361425 down hsa-miR-23c 4.38E−05 1.61E−06 3.371118 down hsa-miR-454-3p 0.002884 2.92E−04 2.117273 down hsa-miR-331-3p 9.31E−04 6.97E−05 2.204815 down hsa-miR-5011-5p 2.97E−04 1.72E−05 2.12799 down hsa-miR-26b-5p 2.04E−06 3.86E−08 2.913235 down hsa-miR-27b-3p 0.001741 1.55E−04 2.012356 down hsa-miR-4753-5p 0.004079 4.41E−04 2.579822 down hsa-miR-3667-5p 2.33E−04 1.30E−05 3.667121 down hsa-miR-601 1.18E−05 3.53E−07 3.796099 down hsa-miR-595 0.001086 8.75E−05 2.123006 down hsa-miR-3149 1.18E−04 5.33E−06 2.016392 down hsa-miR-568 6.07E−04 4.06E−05 2.588553 down hsa-miR-4700-5p 1.30E−04 5.95E−06 3.067645 down hsa-miR-4701-3p 9.92E−05 4.18E−06 2.177509 down hsa-miR-1273f 0.0043 4.76E−04 2.661917 down hsa-miR-3156-5p 0.029612 0.005149 2.818424 down hsa-miR-4489 0.008561 0.001149 2.226271 down hsa-miR-1538 0.022286 0.003652 2.002481 up hsa-miR-602 4.35E−05 1.56E−06 3.929173 up hsa-miR-152 0.001604 1.39E−04 2.903808 up hsa-miR-361-3p 0.004367 4.85E−04 2.0819 up hsa-miR-660-5p 4.69E−04 2.94E−05 3.816789 up hsa-miR-532-3p 8.07E−04 5.87E−05 2.41832 up hsa-miR-532-5p 0.001258 1.04E−04 2.049957 up hsa-miR-500a-5p 1.92E−04 1.01E−05 2.592344 up hsa-miR-502-3p 2.08E−06 4.05E−08 2.867455 up hsa-miR-500a-3p 2.81E−06 5.78E−08 3.288926 up hsa-miR-500b 4.14E−06 9.15E−08 3.306766 up hsa-miR-146b-5p 9.66E−07 1.42E−08 6.577508 up hsa-miR-4758-3p 0.007443 9.27E−04 2.140459 up hsa-miR-572 0.007836 9.83E−04 2.122475 up hsa-miR-4707-3p 2.78E−04 1.58E−05 2.890006 up hsa-miR-4467 1.45E−05 4.59E−07 3.269783 up hsa-miR-211-3p 0.01656 0.002572 2.137208 up hsa-miR-514b-5p 4.14E−06 9.46E−08 2.451853 up hsa-miR-584-5p 2.00E−06 3.68E−08 4.172919 up hsa-miR-140-3p 2.94E−04 1.69E−05 2.380397 up hsa-miR-185-5p 1.18E−05 3.55E−07 2.566472 up hsa-miR-4306 9.11E−06 2.59E−07 2.30042 up hsa-miR-33b-3p 0.001779 1.60E−04 2.029654 up hsa-miR-718 0.001673 1.47E−04 2.019544 up hsa-miR-513a-5p 1.57E−05 5.28E−07 2.5497 up hsa-miR-3180 1.03E−04 4.44E−06 2.207144 up hsa-miR-4634 7.24E−08 5.72E−10 2.783406 up hsa-miR-187-5p 4.38E−05 1.62E−06 2.040338 up hsa-miR-3195 8.82E−10 3.25E−12 3.056716 up hsa-miR-4532 1.08E−08 5.66E−11 2.402943 up hsa-miR-4497 3.24E−07 3.58E−09 2.42442 up hsa-miR-3196 1.22E−08 7.09E−11 2.634303 up hsa-miR-4488 2.77E−07 2.77E−09 2.560814 up hsa-miR-3940-5p 7.03E−06 1.89E−07 2.071707 up hsa-miR-4466 4.49E−06 1.09E−07 2.079208 up hsa-miR-4707-5p 7.75E−06 2.16E−07 2.062567 up hsa-miR-3178 1.96E−06 3.51E−08 3.343691 up hsa-miR-1469 1.21E−05 3.71E−07 2.23717 up hsa-miR-663a 1.75E−07 1.49E−09 2.543887 up hsa-miR-4440 0.013465 0.002015 2.267684 up hsa-miR-4487 0.007933 0.001024 2.725489 up hsa-miR-3907 7.04E−06 1.93E−07 6.43762 up hsa-miR-501-3p 6.88E−07 9.19E−09 6.462504 up hsa-miR-5587-5p 3.80E−04 2.30E−05 5.271441 up hsa-miR-4706 0.029612 0.005149 2.311177 up hsa-miR-5008-5p 0.011546 0.001703 3.408319 up hsa-miR-628-3p 0.02025 0.003233 4.679494 up hsa-miR-3184-5p 0.001423 1.23E−04 8.294511 up hsa-miR-3194-3p 0.002812 2.83E−04 4.127772 up hsa-miR-3619-3p 0.008931 0.001214 4.414144 up hsa-miR-508-5p 6.88E−07 9.42E−09 21.59629 up hsa-miR-218-2-3p 7.74E−04 5.43E−05 7.18456 up hsa-miR-4323 9.31E−04 7.02E−05 4.921013 up hsa-miR-636 0.032688 0.005821 2.65164 up hsa-miR-3944-5p 0.001908 1.74E−04 4.912326 up hsa-miR-4665-3p 5.11E−04 3.26E−05 4.010174 up hsa-miR-181a-3p 0.022309 0.003667 3.537355 up hsa-miR-30b-3p 0.047798 0.009016 2.869683 up hsa-miR-362-3p 2.02E−04 1.08E−05 6.514883 up hsa-miR-501-5p 0.002222 2.07E−04 5.113965 up hsa-miR-140-5p 0.002789 2.73E−04 7.660998 up hsa-miR-584-3p 9.67E−05 4.02E−06 10.36175 up hsa-miR-10a-3p 0.001258 1.04E−04 15.86066 down hsa-miR-196b-5p 1.03E−04 4.44E−06 23.30624 down hsa-miR-654-3p 0.040924 0.007482 5.409095 down hsa-miR-3713 0.006907 8.44E−04 9.587804 down hsa-miR-3170 5.75E−04 3.75E−05 8.304263 down hsa-miR-4672 1.31E−04 6.27E−06 22.65654 down hsa-miR-215 3.54E−04 2.11E−05 30.10806 down hsa-miR-206 3.85E−04 2.35E−05 18.04436 down hsa-miR-539-5p 1.20E−06 2.03E−08 43.96934 down hsa-miR-4311 0.010702 0.001551 5.535323 down hsa-miR-2278 0.001325 1.12E−04 8.703324 down hsa-miR-297 0.010271 0.001461 9.661793 down

TABLE 9 The top up and down regulated miRNAs by at least 10 fold in melanoma Reg- GeneName p (Corr) p FC (abs) ulation hsa-miR-211-5p 3.43E−07 3.97E−09 276.84088 up hsa-miR-514a- 2.96E−07 3.12E−09 204.08942 up 3p hsa-miR-509-3p 5.15E−07 6.24E−09 139.35081 up hsa-miR-204-5p 5.50E−06 1.39E−07 93.670715 up hsa-miR-509-5p 2.29E−06 4.59E−08 65.2407 up hsa-miR-513b 1.08E−05 3.13E−07 61.259735 up hsa-miR-509-3- 3.46E−06 7.48E−08 55.923203 up 5p hsa-miR-145-5p 1.31E−04 6.27E−06 54.31673 up hsa-miR-146a-5p 3.50E−10 5.54E−13 36.32244 up hsa-miR-508-3p 7.57E−05 3.03E−06 31.908644 up hsa-miR-506-3p 1.47E−04 7.38E−06 27.691364 up hsa-miR-513c- 1.56E−04 7.97E−06 26.930325 up 5p hsa-miR-4731- 1.47E−04 7.29E−06 24.178703 up 5p hsa-miR-508-5p 6.88E−07 9.42E−09 21.59629 up hsa-miR-363-3p 0.001080965 8.32E−05 17.002277 up hsa-miR-551b- 0.002793076 2.75E−04 13.102462 up 3p hsa-miR-4708- 0.001086084 8.76E−05 12.242638 up 3p hsa-miR-510 0.001869193 1.69E−04 11.336461 up hsa-miR-584-3p 9.67E−05 4.02E−06 10.361748 up hsa-miR-5701 0.008838477 0.00119678 10.0155525 up hsa-miR-617 1.49E−05 4.87E−07 10.126807 down hsa-miR-299-3p 0.001144688 9.29E−05 10.297629 down hsa-miR-3125 1.56E−05 5.17E−07 10.459817 down hsa-miR-4768- 3.71E−04 2.23E−05 11.119021 down 3p hsa-miR-4719 0.00707513 8.69E−04 11.243696 down hsa-miR-3682- 8.17E−05 3.31E−06 13.372626 down 3p hsa-miR-1183 4.14E−06 9.60E−08 13.502589 down hsa-miR-200c-5p 2.14E−04 1.17E−05 13.616578 down hsa-miR-2115- 1.64E−04 8.45E−06 13.621608 down 3p hsa-miR-1244 1.30E−05 4.04E−07 13.876072 down hsa-miR-10a-3p 0.001257804 1.04E−04 15.860663 down hsa-miR-182-5p 1.59E−10 1.67E−13 16.291529 down hsa-miR-206 3.85E−04 2.35E−05 18.044355 down hsa-miR-205-5p 0.001083732 8.52E−05 19.473736 down hsa-miR-4672 1.31E−04 6.27E−06 22.656536 down hsa-miR-31-3p 0.001083732 8.62E−05 22.977182 down hsa-miR-196b- 1.03E−04 4.44E−06 23.306242 down 5p hsa-miR-29b-2- 4.74E−06 1.17E−07 24.812508 down 5p hsa-miR-3120- 9.80E−07 1.50E−08 25.58355 down 3p hsa-miR-452-5p 3.02E−06 6.36E−08 28.705942 down hsa-miR-215 3.54E−04 2.11E−05 30.10806 down hsa-miR-200a-5p 4.47E−06 1.06E−07 31.411602 down hsa-miR-375 1.84E−05 6.29E−07 34.56165 down hsa-miR-335-3p 9.59E−07 1.36E−08 40.526886 down hsa-miR-539-5p 1.20E−06 2.03E−08 43.96934 down hsa-miR-224-5p 1.05E−06 1.66E−08 93.71726 down hsa-miR-183-3p 4.11E−09 1.73E−11 101.033875 down hsa-miR-96-5p 1.73E−08 1.10E−10 120.48337 down hsa-miR-335-5p 2.00E−07 1.89E−09 138.42706 down hsa-miR-141-3p 1.08E−08 5.70E−11 206.62756 down hsa-miR-183-5p 3.29E−11 1.73E−14 220.74986 down hsa-miR-200c-3p 1.29E−06 2.24E−08 250.74672 down hsa-miR-203 2.10E−08 1.44E−10 273.66293 down hsa-miR-429 3.83E−10 9.84E−13 515.76654 down hsa-miR-200a-3p 6.01E−10 1.90E−12 680.57495 down hsa-miR-200b- 3.83E−10 1.01E−12 1717.0214 down 3p

TABLE 10 Cell lines in which quantitative real-time PCR validation was performed qRT PCR Sample ID Type Cohort validation QF1160MB Melanoblast Discovery Yes MELA Melanocyte Discovery Yes MM653 Nevus Discovery Yes 22rV-1 Other Discovery Yes 786-O Other Discovery Yes ALVA-1 Other Discovery Yes BT474 Other Discovery Yes BxPC-3 Other Discovery Yes CAKI-1 Other Discovery Yes CAKI-2 Other Discovery Yes CaOV-3 Other Discovery Yes CAPAN-1 Other Discovery Yes CAPAN-2 Other Discovery Yes Co115 Other Discovery Yes Du145 Other Discovery Yes HT29 Other Discovery Yes LIM1899 Other Discovery Yes LIM2405 Other Discovery Yes LN-18 Other Discovery Yes LNCAP Other Discovery Yes MCF-7 Other Discovery Yes MDAMB231 Other Discovery Yes OAW28 Other Discovery Yes OVCAR-3 Other Discovery Yes PANC-1 Other Discovery Yes PC-3 Other Discovery Yes PE04 Other Discovery Yes PL45 Other Discovery Yes SKBR3 Other Discovery Yes SK-OV-3 Other Discovery Yes SN12K-1 Other Discovery Yes SW48 Other Discovery Yes SW839 Other Discovery Yes T46 Other Discovery Yes T47D Other Discovery Yes T50 Other Discovery Yes U118 Other Discovery Yes MM200 STAGE I/II Validation Yes MM229 STAGE I/II Validation Yes MM329 STAGE I/II Validation Yes MM540 STAGE I/II Validation Yes C001 STAGE_III_MM Discovery Yes C002 STAGE_III_MM Discovery Yes C003 STAGE_III_MM Discovery Yes C004 STAGE_III_MM Discovery Yes C006 STAGE_III_MM Discovery Yes C008 STAGE_III_MM Discovery Yes C011 STAGE_III_MM Discovery Yes C012 STAGE_III_MM Discovery Yes C013 STAGE_III_MM Discovery Yes C016 STAGE_III_MM Discovery Yes C017 STAGE_III_MM Discovery Yes C021 STAGE_III_MM Discovery Yes C022 STAGE_III_MM Discovery Yes C025 STAGE_III_MM Discovery Yes C027 STAGE_III_MM Discovery Yes C028 STAGE_III_MM Discovery Yes C037 STAGE_III_MM Discovery Yes C038 STAGE_III_MM Discovery Yes C042 STAGE_III_MM Discovery Yes C043 STAGE_III_MM Discovery Yes C044 STAGE_III_MM Discovery Yes C045 STAGE_III_MM Discovery Yes C050 STAGE_III_MM Discovery Yes C052 STAGE_III_MM Discovery Yes C054 STAGE_III_MM Discovery Yes C057 STAGE_III_MM Discovery Yes C058 STAGE_III_MM Discovery Yes C060 STAGE_III_MM Discovery Yes C062 STAGE_III_MM Discovery Yes C065 STAGE_III_MM Discovery Yes C067 STAGE_III_MM Discovery Yes C071 STAGE_III_MM Discovery Yes C074 STAGE_III_MM Discovery Yes C076 STAGE_III_MM Discovery Yes C077 STAGE_III_MM Discovery Yes C078 STAGE_III_MM Discovery Yes C079 STAGE_III_MM Discovery Yes C080 STAGE_III_MM Discovery Yes C081 STAGE_III_MM Discovery Yes C083 STAGE_III_MM Discovery Yes C084 STAGE_III_MM Discovery Yes C086 STAGE_III_MM Discovery Yes C088 STAGE_III_MM Discovery Yes C089 STAGE_III_MM Discovery Yes C091 STAGE_III_MM Discovery Yes C092 STAGE_III_MM Discovery Yes C094 STAGE_III_MM Discovery Yes C096 STAGE_III_MM Discovery Yes C097 STAGE_III_MM Discovery Yes C100 STAGE_III_MM Discovery Yes C106 STAGE_III_MM Discovery Yes C108 STAGE_III_MM Discovery Yes A02 STAGE_IV_MM Discovery Yes A04 STAGE_IV_MM Discovery Yes A06 STAGE_IV_MM Validation Yes A13 STAGE_IV_MM Validation Yes A15 STAGE_IV_MM Validation Yes AF6 STAGE_IV_MM Validation Yes C-32 STAGE_IV_MM Validation Yes CJM STAGE_IV_MM Validation Yes D01 STAGE_IV_MM Validation Yes D04 STAGE_IV_MM Validation Yes D05 STAGE_IV_MM Validation Yes D05 STAGE_IV_MM Validation Yes D10 STAGE_IV_MM Validation Yes D11 STAGE_IV_MM Validation Yes D14 STAGE_IV_MM Validation Yes D17 STAGE_IV_MM Validation Yes D20 STAGE_IV_MM Validation Yes D22 STAGE_IV_MM Validation Yes D24 STAGE_IV_MM Validation Yes D25 STAGE_IV_MM Validation Yes D28 STAGE_IV_MM Validation Yes D29 STAGE_IV_MM Validation Yes D32 STAGE_IV_MM Validation Yes D35 STAGE_IV_MM Validation Yes D36 STAGE_IV_MM Validation Yes D40 STAGE_IV_MM Validation Yes D41 STAGE_IV_MM Validation Yes D59 STAGE_IV_MM Discovery Yes HT144 STAGE_IV_MM Validation Yes MM127 STAGE_IV_MM Validation Yes MM253 STAGE_IV_MM Validation Yes MM370 STAGE_IV_MM Validation Yes MM386 STAGE_IV_MM Validation Yes MM415 STAGE_IV_MM Validation Yes MM426 STAGE_IV_MM Validation Yes MM466 STAGE_IV_MM Validation Yes MM473 STAGE_IV_MM Validation Yes MM485 STAGE_IV_MM Validation Yes MM537 STAGE_IV_MM Validation Yes MM548 STAGE_IV_MM Validation Yes MM649 STAGE_IV_MM Validation Yes MM96L STAGE_IV_MM Validation Yes SKMEL28 STAGE_IV_MM Validation Yes SKMEL5 STAGE_IV_MM Validation Yes 92_1 Uveal MM Discovery Yes MEL202 Uveal MM Discovery Yes MEL270 Uveal MM Discovery Yes MEL285 Uveal MM Discovery Yes MEL290 Uveal MM Discovery Yes OCM8 Uveal MM Discovery Yes OMM1 Uveal MM Discovery Yes

Throughout the specification the aim has been to describe the preferred embodiments of the invention without limiting the invention to any one embodiment or specific collection of features. It will therefore be appreciated by those of skill in the art that, in light of the instant disclosure, various modifications and changes can be made in the particular embodiments exemplified without departing from the scope of the present invention.

All computer programs, algorithms, patent and scientific literature referred to herein is incorporated herein by reference.

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1. A method of determining whether or not a subject has melanoma, including: determining an expression level of one or more miRNA biomarkers in a biological sample from a subject, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, or a fragment or variant thereof, and wherein melanoma is detected if said expression level of one or more miRNA biomarkers is altered or modulated in the biological sample.
 2. A method of determining the prognosis of a subject with melanoma, including: determining an expression level of one or more miRNA biomarkers in a biological sample obtained from the subject, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, or a fragment or variant thereof, to thereby evaluate the prognosis of melanoma in the subject.
 3. The method of claim 2, wherein if the expression level of said one or more miRNA biomarkers is altered or modulated in the biological sample, the prognosis may be negative or positive.
 4. The method of claim 3, wherein the prognosis is used, at least in part, to determine whether the subject would benefit from treatment of melanoma.
 5. The method of claim 3, wherein the prognosis is used, at least in part, to develop a treatment strategy for the subject.
 6. The method of claim 2, wherein the prognosis is used, at least in part, to determine disease progression in the subject.
 7. The method of claim 2, wherein prognosis is defined as an estimated time of survival.
 8. The method of claim 2, further including determining suitability of the subject for treatment based, at least in part, on the prognosis.
 9. The method of claim 1, further including determining a disease stage and/or grade for melanoma based on, at least in part, the expression level of the one or more miRNA biomarkers.
 10. The method of claim 1, wherein the expression level of the one or more miRNA biomarkers is determined before, during and/or after treatment.
 11. A method of treating melanoma in a subject including; determining an expression level of one or more miRNA biomarkers in a biological sample from the subject, before, during and/or after treatment of melanoma, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, or a fragment or variant thereof, and based on the determination made, initiating, continuing, modifying or discontinuing a treatment of melanoma.
 12. A method of evaluating treatment efficacy of melanoma in a subject including: determining an expression level of one or more miRNA biomarkers in a biological sample from the subject before, during and/or after treatment, wherein the one or more miRNA biomarkers are selected from the group consisting of miRNA-4487, miRNA-4706 and miRNA-4731, or a fragment or variant thereof; and determining whether or not the treatment is efficacious according to whether said expression level of one or more miRNA biomarkers is altered or modulated in the subject's biological sample.
 13. The method of claim 11, further including selecting a treatment for melanoma based on the expression level of the one or more miRNA biomarkers.
 14. The method of claim 11, further including measuring an expression level of one or more additional miRNA biomarkers selected from the group consisting of miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p, or a fragment or variant thereof. 15-20. (canceled)
 21. The method of claim 1, further including measuring an expression level of one or more additional miRNA biomarkers selected from the group consisting of miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p, or a fragment or variant thereof.
 22. The method of claim 2, further including determining a disease stage and/or grade for melanoma based on, at least in part, the expression level of the one or more miRNA biomarkers.
 23. The method of claim 2, wherein the expression level of the one or more miRNA biomarkers is determined before, during and/or after treatment.
 24. The method of claim 2, further including measuring an expression level of one or more additional miRNA biomarkers selected from the group consisting of miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p, or a fragment or variant thereof.
 25. The method of claim 12, further including selecting a treatment for melanoma based on the expression level of the one or more miRNA biomarkers.
 26. The method of claim 12, further including measuring an expression level of one or more additional miRNA biomarkers selected from the group consisting of miRNA-16, miRNA-211, miRNA-509-3p and miRNA-509-5p, or a fragment or variant thereof. 